{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "regular-tourism",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Using CellTypist for cell type classification\n",
    "This notebook showcases the cell type classification for scRNA-seq query data by retrieving the most likely cell type labels from either the built-in CellTypist models or the user-trained custom models."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sticky-reaction",
   "metadata": {},
   "source": [
    "Only the main steps and key parameters are introduced in this notebook. Refer to detailed [Usage](https://github.com/Teichlab/celltypist#usage) if you want to learn more."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "marked-northeast",
   "metadata": {},
   "source": [
    "## Install CellTypist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "pleasant-basis",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting celltypist\n",
      "  Downloading celltypist-0.1.9-py3-none-any.whl (5.0 MB)\n",
      "\u001b[K     |████████████████████████████████| 5.0 MB 16.4 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: click>=7.1.2 in /opt/conda/lib/python3.8/site-packages (from celltypist) (7.1.2)\n",
      "Requirement already satisfied: leidenalg>=0.8.3 in /opt/conda/lib/python3.8/site-packages (from celltypist) (0.8.3)\n",
      "Requirement already satisfied: openpyxl>=3.0.4 in /opt/conda/lib/python3.8/site-packages (from celltypist) (3.0.7)\n",
      "Requirement already satisfied: scanpy>=1.7.0 in /opt/conda/lib/python3.8/site-packages (from celltypist) (1.7.1)\n",
      "Requirement already satisfied: pandas>=1.0.5 in /opt/conda/lib/python3.8/site-packages (from celltypist) (1.2.3)\n",
      "Requirement already satisfied: numpy>=1.19.0 in /opt/conda/lib/python3.8/site-packages (from celltypist) (1.20.1)\n",
      "Requirement already satisfied: scikit-learn>=0.24.1 in /opt/conda/lib/python3.8/site-packages (from celltypist) (0.24.1)\n",
      "Requirement already satisfied: requests>=2.23.0 in /opt/conda/lib/python3.8/site-packages (from celltypist) (2.25.1)\n",
      "Requirement already satisfied: et-xmlfile in /opt/conda/lib/python3.8/site-packages (from openpyxl>=3.0.4->celltypist) (1.0.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.8/site-packages (from pandas>=1.0.5->celltypist) (2.8.1)\n",
      "Requirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.8/site-packages (from pandas>=1.0.5->celltypist) (2021.1)\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas>=1.0.5->celltypist) (1.15.0)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.8/site-packages (from requests>=2.23.0->celltypist) (1.26.3)\n",
      "Requirement already satisfied: chardet<5,>=3.0.2 in /opt/conda/lib/python3.8/site-packages (from requests>=2.23.0->celltypist) (4.0.0)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.8/site-packages (from requests>=2.23.0->celltypist) (2020.12.5)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.8/site-packages (from requests>=2.23.0->celltypist) (2.10)\n",
      "Requirement already satisfied: patsy in /opt/conda/lib/python3.8/site-packages (from scanpy>=1.7.0->celltypist) (0.5.1)\n",
      "Requirement already satisfied: anndata>=0.7.4 in /opt/conda/lib/python3.8/site-packages (from scanpy>=1.7.0->celltypist) (0.7.5)\n",
      "Requirement already satisfied: h5py>=2.10.0 in /opt/conda/lib/python3.8/site-packages (from scanpy>=1.7.0->celltypist) (3.1.0)\n",
      "Requirement already satisfied: matplotlib>=3.1.2 in /opt/conda/lib/python3.8/site-packages (from scanpy>=1.7.0->celltypist) (3.3.4)\n",
      "Requirement already satisfied: natsort in /opt/conda/lib/python3.8/site-packages (from scanpy>=1.7.0->celltypist) (7.1.1)\n",
      "Requirement already satisfied: joblib in /opt/conda/lib/python3.8/site-packages (from scanpy>=1.7.0->celltypist) (1.0.1)\n",
      "Requirement already satisfied: seaborn in /opt/conda/lib/python3.8/site-packages (from scanpy>=1.7.0->celltypist) (0.11.1)\n",
      "Requirement already satisfied: scipy>=1.4 in /opt/conda/lib/python3.8/site-packages (from scanpy>=1.7.0->celltypist) (1.6.1)\n",
      "Requirement already satisfied: legacy-api-wrap in /opt/conda/lib/python3.8/site-packages (from scanpy>=1.7.0->celltypist) (0.0.0)\n",
      "Requirement already satisfied: umap-learn>=0.3.10 in /opt/conda/lib/python3.8/site-packages (from scanpy>=1.7.0->celltypist) (0.4.6)\n",
      "Requirement already satisfied: packaging in /opt/conda/lib/python3.8/site-packages (from scanpy>=1.7.0->celltypist) (20.9)\n",
      "Requirement already satisfied: statsmodels>=0.10.0rc2 in /opt/conda/lib/python3.8/site-packages (from scanpy>=1.7.0->celltypist) (0.12.2)\n",
      "Requirement already satisfied: numba>=0.41.0 in /opt/conda/lib/python3.8/site-packages (from scanpy>=1.7.0->celltypist) (0.51.2)\n",
      "Requirement already satisfied: sinfo in /opt/conda/lib/python3.8/site-packages (from scanpy>=1.7.0->celltypist) (0.3.1)\n",
      "Requirement already satisfied: networkx>=2.3 in /opt/conda/lib/python3.8/site-packages (from scanpy>=1.7.0->celltypist) (2.5)\n",
      "Requirement already satisfied: tqdm in /opt/conda/lib/python3.8/site-packages (from scanpy>=1.7.0->celltypist) (4.58.0)\n",
      "Requirement already satisfied: tables in /opt/conda/lib/python3.8/site-packages (from scanpy>=1.7.0->celltypist) (3.6.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.8/site-packages (from matplotlib>=3.1.2->scanpy>=1.7.0->celltypist) (1.3.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /opt/conda/lib/python3.8/site-packages (from matplotlib>=3.1.2->scanpy>=1.7.0->celltypist) (2.4.7)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.8/site-packages (from matplotlib>=3.1.2->scanpy>=1.7.0->celltypist) (8.1.2)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.8/site-packages (from matplotlib>=3.1.2->scanpy>=1.7.0->celltypist) (0.10.0)\n",
      "Requirement already satisfied: decorator>=4.3.0 in /opt/conda/lib/python3.8/site-packages (from networkx>=2.3->scanpy>=1.7.0->celltypist) (4.4.2)\n",
      "Requirement already satisfied: setuptools in /opt/conda/lib/python3.8/site-packages (from numba>=0.41.0->scanpy>=1.7.0->celltypist) (49.6.0.post20210108)\n",
      "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /opt/conda/lib/python3.8/site-packages (from numba>=0.41.0->scanpy>=1.7.0->celltypist) (0.34.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.8/site-packages (from scikit-learn>=0.24.1->celltypist) (2.1.0)\n",
      "Requirement already satisfied: get-version>=2.0.4 in /opt/conda/lib/python3.8/site-packages (from legacy-api-wrap->scanpy>=1.7.0->celltypist) (2.1)\n",
      "Requirement already satisfied: stdlib-list in /opt/conda/lib/python3.8/site-packages (from sinfo->scanpy>=1.7.0->celltypist) (0.7.0)\n",
      "Requirement already satisfied: numexpr>=2.6.2 in /opt/conda/lib/python3.8/site-packages (from tables->scanpy>=1.7.0->celltypist) (2.7.3)\n",
      "Installing collected packages: celltypist\n",
      "Successfully installed celltypist-0.1.9\n"
     ]
    }
   ],
   "source": [
    "!pip install celltypist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "polyphonic-matter",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scanpy as sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "apart-comment",
   "metadata": {},
   "outputs": [],
   "source": [
    "import celltypist\n",
    "from celltypist import models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "outside-newman",
   "metadata": {},
   "source": [
    "## Download a scRNA-seq dataset of 2,000 immune cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "retained-story",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f4e65b2ee2214c0a9f511b94344ec8a9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0.00/34.1M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adata_2000 = sc.read('celltypist_demo_folder/demo_2000_cells.h5ad', backup_url = 'https://celltypist.cog.sanger.ac.uk/Notebook_demo_data/demo_2000_cells.h5ad')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alert-officer",
   "metadata": {},
   "source": [
    "This dataset includes 2,000 cells and 18,950 genes collected from different studies, thereby showing the practical applicability of CellTypist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "spatial-serve",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 18950)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_2000.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "irish-edmonton",
   "metadata": {
    "tags": []
   },
   "source": [
    "The expression matrix (`adata_2000.X`) is pre-processed (and required) as log1p normalised expression to 10,000 counts per cell (this matrix can be alternatively stashed in `.raw.X`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "twelve-withdrawal",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[10000.   ],\n",
       "        [10000.002],\n",
       "        [10000.   ],\n",
       "        ...,\n",
       "        [10000.   ],\n",
       "        [10000.   ],\n",
       "        [10000.   ]], dtype=float32)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_2000.X.expm1().sum(axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "secure-smile",
   "metadata": {},
   "source": [
    "Some pre-assigned cell type labels are also in the data, which will be compared to the predicted labels from CellTypist later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "standard-navigation",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cell_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cell1</th>\n",
       "      <td>Plasma cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell2</th>\n",
       "      <td>Plasma cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell3</th>\n",
       "      <td>Plasma cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell4</th>\n",
       "      <td>Plasma cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell5</th>\n",
       "      <td>Plasma cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell1996</th>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell1997</th>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell1998</th>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell1999</th>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell2000</th>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              cell_type\n",
       "cell1                      Plasma cells\n",
       "cell2                      Plasma cells\n",
       "cell3                      Plasma cells\n",
       "cell4                      Plasma cells\n",
       "cell5                      Plasma cells\n",
       "...                                 ...\n",
       "cell1996  Neutrophil-myeloid progenitor\n",
       "cell1997  Neutrophil-myeloid progenitor\n",
       "cell1998  Neutrophil-myeloid progenitor\n",
       "cell1999  Neutrophil-myeloid progenitor\n",
       "cell2000  Neutrophil-myeloid progenitor\n",
       "\n",
       "[2000 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_2000.obs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efficient-technology",
   "metadata": {},
   "source": [
    "## Assign cell type labels using a CellTypist built-in model\n",
    "In this section, we show the procedure of transferring cell type labels from built-in models to the query dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "polish-keeping",
   "metadata": {},
   "source": [
    "Download the latest CellTypist models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "improved-edwards",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "📜 Retrieving model list from server https://celltypist.cog.sanger.ac.uk/models/models.json\n",
      "📚 Total models in list: 7\n",
      "📂 Storing models in /home/jovyan/.celltypist/data/models\n",
      "💾 Downloading model [1/7]: Immune_All_Low.pkl\n",
      "💾 Downloading model [2/7]: Immune_All_High.pkl\n",
      "💾 Downloading model [3/7]: Immune_All_PIP.pkl\n",
      "💾 Downloading model [4/7]: Immune_All_AddPIP.pkl\n",
      "💾 Downloading model [5/7]: Cells_Intestinal_Tract.pkl\n",
      "💾 Downloading model [6/7]: Cells_Lung_Airway.pkl\n",
      "💾 Downloading model [7/7]: Nuclei_Lung_Airway.pkl\n"
     ]
    }
   ],
   "source": [
    "# Enabling `force_update = True` will overwrite existing (old) models.\n",
    "models.download_models(force_update = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "about-ballet",
   "metadata": {},
   "source": [
    "All models are stored in `models.models_path`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "tired-taylor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/jovyan/.celltypist/data/models'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models.models_path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "likely-language",
   "metadata": {},
   "source": [
    "Get an overview of the models and what they represent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "corrected-authentication",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "👉 Detailed model information can be found at `https://www.celltypist.org/models`\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Immune_All_Low.pkl</td>\n",
       "      <td>immune sub-populations combined from 20 tissue...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Immune_All_High.pkl</td>\n",
       "      <td>immune populations combined from 20 tissues of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Immune_All_PIP.pkl</td>\n",
       "      <td>immune cell types combined from 16 adult human...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Immune_All_AddPIP.pkl</td>\n",
       "      <td>immune cell types combined from &gt;20 human tiss...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cells_Intestinal_Tract.pkl</td>\n",
       "      <td>intestinal cells from fetal, pediatric and adu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cells_Lung_Airway.pkl</td>\n",
       "      <td>cell populations from scRNA-seq of five locati...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nuclei_Lung_Airway.pkl</td>\n",
       "      <td>cell populations from snRNA-seq of five locati...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        model  \\\n",
       "0          Immune_All_Low.pkl   \n",
       "1         Immune_All_High.pkl   \n",
       "2          Immune_All_PIP.pkl   \n",
       "3       Immune_All_AddPIP.pkl   \n",
       "4  Cells_Intestinal_Tract.pkl   \n",
       "5       Cells_Lung_Airway.pkl   \n",
       "6      Nuclei_Lung_Airway.pkl   \n",
       "\n",
       "                                         description  \n",
       "0  immune sub-populations combined from 20 tissue...  \n",
       "1  immune populations combined from 20 tissues of...  \n",
       "2  immune cell types combined from 16 adult human...  \n",
       "3  immune cell types combined from >20 human tiss...  \n",
       "4  intestinal cells from fetal, pediatric and adu...  \n",
       "5  cell populations from scRNA-seq of five locati...  \n",
       "6  cell populations from snRNA-seq of five locati...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models.models_description()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "living-appendix",
   "metadata": {},
   "source": [
    "Choose the model you want to employ, for example, the model with all tissues combined containing low-hierarchy (high-resolution) cell types/subtypes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "laughing-iraqi",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Indeed, the `model` argument defaults to `Immune_All_Low.pkl`.\n",
    "model = models.Model.load(model = 'Immune_All_Low.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "silent-clause",
   "metadata": {},
   "source": [
    "This model contains 91 cell states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "gross-alaska",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['B cells', 'CD16+ NK cells', 'CD16- NK cells', 'CD8a/a',\n",
       "       'CD8a/b(entry)', 'CMP', 'Classical monocytes', 'Cycling B cells',\n",
       "       'Cycling DCs', 'Cycling NK cells', 'Cycling T cells',\n",
       "       'Cycling gamma-delta T cells', 'Cycling monocytes',\n",
       "       'Cytotoxic T cells', 'DC', 'DC precursor', 'DC1', 'DC2', 'DC3',\n",
       "       'Double-negative thymocytes', 'Double-positive thymocytes', 'ELP',\n",
       "       'ETP', 'Early MK', 'Early erythroid', 'Early lymphoid/T lymphoid',\n",
       "       'Endothelial cells', 'Epithelial cells', 'Erythrocytes',\n",
       "       'Fibroblasts', 'Follicular B cells', 'Follicular helper T cells',\n",
       "       'GMP', 'Germinal center B cells', 'Granulocytes', 'HSC/MPP',\n",
       "       'Helper T cells', 'Hofbauer cells', 'ILC', 'ILC precursor', 'ILC1',\n",
       "       'ILC2', 'ILC3', 'Immature B cells', 'Kidney-resident macrophages',\n",
       "       'Kupffer cells', 'Late erythroid', 'MAIT cells', 'MEMP', 'MNP',\n",
       "       'Macrophages', 'Mast cells', 'Megakaryocyte precursor',\n",
       "       'Megakaryocyte-erythroid-mast cell progenitor',\n",
       "       'Megakaryocytes/platelets', 'Memory B cells',\n",
       "       'Memory CD4+ cytotoxic T cells', 'Mid erythroid', 'Migratory DCs',\n",
       "       'Mono-mac', 'Monocyte precursor', 'Monocytes', 'Myelocytes',\n",
       "       'NK cells', 'NKT cells', 'Naive B cells',\n",
       "       'Neutrophil-myeloid progenitor', 'Neutrophils',\n",
       "       'Non-classical monocytes', 'Plasma cells', 'Pre-B cells',\n",
       "       'Pre-pro-B cells', 'Pro-B cells', 'Promyelocytes',\n",
       "       'Regulatory T cells', 'T cells', 'T(agonist)',\n",
       "       'Tcm/Naive cytotoxic T cells', 'Tcm/Naive helper T cells',\n",
       "       'Tem/Effector cytotoxic T cells', 'Tem/Effector helper T cells',\n",
       "       'Tem/Effector helper T cells PD1+', 'Transitional B cells',\n",
       "       'Transitional DC', 'Transitional NK', 'Treg(diff)',\n",
       "       'Type 1 helper T cells', 'Type 17 helper T cells',\n",
       "       'gamma-delta T cells', 'pDC', 'pDC precursor'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.cell_types"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "existing-tsunami",
   "metadata": {
    "tags": []
   },
   "source": [
    "Some model meta-information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "statewide-exclusion",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'date': '2021-10-27 15:20:55.163288',\n",
       " 'details': 'immune sub-populations combined from 20 tissues of 19 studies',\n",
       " 'url': 'https://celltypist.cog.sanger.ac.uk/models/Pan_Immune_CellTypist/v1/Immune_All_Low.pkl',\n",
       " 'source': 'https://doi.org/10.1101/2021.04.28.441762',\n",
       " 'version': 'v1',\n",
       " 'number_celltypes': 91}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.description"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "quick-virus",
   "metadata": {
    "tags": []
   },
   "source": [
    "Transfer cell type labels from this model to the query dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "educated-feature",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "🔬 Input data has 2000 cells and 18950 genes\n",
      "🔗 Matching reference genes in the model\n",
      "🧬 3278 features used for prediction\n",
      "⚖️ Scaling input data\n",
      "🖋️ Predicting labels\n",
      "✅ Prediction done!\n",
      "👀 Can not detect a neighborhood graph, construct one before the over-clustering\n",
      "⛓️ Over-clustering input data with resolution set to 5\n",
      "🗳️ Majority voting the predictions\n",
      "✅ Majority voting done!\n"
     ]
    }
   ],
   "source": [
    "# Not run; predict cell identities using this loaded model.\n",
    "#predictions = celltypist.annotate(adata_2000, model = model, majority_voting = True)\n",
    "# Alternatively, just specify the model name (recommended as this ensures the model is intact every time it is loaded).\n",
    "predictions = celltypist.annotate(adata_2000, model = 'Immune_All_Low.pkl', majority_voting = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "present-plymouth",
   "metadata": {},
   "source": [
    "By default (`majority_voting = False`), CellTypist will infer the identity of each query cell independently. This leads to raw predicted cell type labels, and usually finishes within seconds or minutes depending on the size of the query data. You can also turn on the majority-voting classifier (`majority_voting = True`), which refines cell identities within local subclusters after an over-clustering approach at the cost of increased runtime."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "naughty-stand",
   "metadata": {
    "tags": []
   },
   "source": [
    "The results include both predicted cell type labels (`predicted_labels`), over-clustering result (`over_clustering`), and predicted labels after majority voting in local subclusters (`majority_voting`). Note in the `predicted_labels`, each query cell gets its inferred label by choosing the most probable cell type among all possible cell types in the given model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "sapphire-equity",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predicted_labels</th>\n",
       "      <th>over_clustering</th>\n",
       "      <th>majority_voting</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cell1</th>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>42</td>\n",
       "      <td>Plasma cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell2</th>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>14</td>\n",
       "      <td>Plasma cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell3</th>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>37</td>\n",
       "      <td>gamma-delta T cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell4</th>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>2</td>\n",
       "      <td>Plasma cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell5</th>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>2</td>\n",
       "      <td>Plasma cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell1996</th>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "      <td>10</td>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell1997</th>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "      <td>28</td>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell1998</th>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "      <td>27</td>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell1999</th>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "      <td>28</td>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell2000</th>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "      <td>10</td>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       predicted_labels over_clustering  \\\n",
       "cell1                      Plasma cells              42   \n",
       "cell2                      Plasma cells              14   \n",
       "cell3                      Plasma cells              37   \n",
       "cell4                      Plasma cells               2   \n",
       "cell5                      Plasma cells               2   \n",
       "...                                 ...             ...   \n",
       "cell1996  Neutrophil-myeloid progenitor              10   \n",
       "cell1997  Neutrophil-myeloid progenitor              28   \n",
       "cell1998  Neutrophil-myeloid progenitor              27   \n",
       "cell1999  Neutrophil-myeloid progenitor              28   \n",
       "cell2000  Neutrophil-myeloid progenitor              10   \n",
       "\n",
       "                        majority_voting  \n",
       "cell1                      Plasma cells  \n",
       "cell2                      Plasma cells  \n",
       "cell3               gamma-delta T cells  \n",
       "cell4                      Plasma cells  \n",
       "cell5                      Plasma cells  \n",
       "...                                 ...  \n",
       "cell1996  Neutrophil-myeloid progenitor  \n",
       "cell1997  Neutrophil-myeloid progenitor  \n",
       "cell1998  Neutrophil-myeloid progenitor  \n",
       "cell1999  Neutrophil-myeloid progenitor  \n",
       "cell2000  Neutrophil-myeloid progenitor  \n",
       "\n",
       "[2000 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.predicted_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "applied-premiere",
   "metadata": {},
   "source": [
    "Transform the prediction result into an `AnnData`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "opponent-samuel",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get an `AnnData` with predicted labels embedded into the cell metadata columns.\n",
    "adata = predictions.to_adata()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "breathing-expense",
   "metadata": {},
   "source": [
    "Compared to `adata_2000`, the new `adata` has additional prediction information in `adata.obs` (`predicted_labels`, `over_clustering`, `majority_voting` and `conf_score`). Of note, all these columns can be prefixed with a specific string by setting `prefix` in `to_adata`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "outdoor-proposal",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cell_type</th>\n",
       "      <th>predicted_labels</th>\n",
       "      <th>over_clustering</th>\n",
       "      <th>majority_voting</th>\n",
       "      <th>conf_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cell1</th>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>42</td>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>0.996814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell2</th>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>14</td>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>0.995119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell3</th>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>37</td>\n",
       "      <td>gamma-delta T cells</td>\n",
       "      <td>0.991911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell4</th>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>2</td>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>0.995159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell5</th>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>2</td>\n",
       "      <td>Plasma cells</td>\n",
       "      <td>0.995717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell1996</th>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "      <td>10</td>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "      <td>0.724281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell1997</th>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "      <td>28</td>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "      <td>0.977658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell1998</th>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "      <td>27</td>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "      <td>0.843348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell1999</th>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "      <td>28</td>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "      <td>0.955746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell2000</th>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "      <td>10</td>\n",
       "      <td>Neutrophil-myeloid progenitor</td>\n",
       "      <td>0.989907</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              cell_type               predicted_labels  \\\n",
       "cell1                      Plasma cells                   Plasma cells   \n",
       "cell2                      Plasma cells                   Plasma cells   \n",
       "cell3                      Plasma cells                   Plasma cells   \n",
       "cell4                      Plasma cells                   Plasma cells   \n",
       "cell5                      Plasma cells                   Plasma cells   \n",
       "...                                 ...                            ...   \n",
       "cell1996  Neutrophil-myeloid progenitor  Neutrophil-myeloid progenitor   \n",
       "cell1997  Neutrophil-myeloid progenitor  Neutrophil-myeloid progenitor   \n",
       "cell1998  Neutrophil-myeloid progenitor  Neutrophil-myeloid progenitor   \n",
       "cell1999  Neutrophil-myeloid progenitor  Neutrophil-myeloid progenitor   \n",
       "cell2000  Neutrophil-myeloid progenitor  Neutrophil-myeloid progenitor   \n",
       "\n",
       "         over_clustering                majority_voting  conf_score  \n",
       "cell1                 42                   Plasma cells    0.996814  \n",
       "cell2                 14                   Plasma cells    0.995119  \n",
       "cell3                 37            gamma-delta T cells    0.991911  \n",
       "cell4                  2                   Plasma cells    0.995159  \n",
       "cell5                  2                   Plasma cells    0.995717  \n",
       "...                  ...                            ...         ...  \n",
       "cell1996              10  Neutrophil-myeloid progenitor    0.724281  \n",
       "cell1997              28  Neutrophil-myeloid progenitor    0.977658  \n",
       "cell1998              27  Neutrophil-myeloid progenitor    0.843348  \n",
       "cell1999              28  Neutrophil-myeloid progenitor    0.955746  \n",
       "cell2000              10  Neutrophil-myeloid progenitor    0.989907  \n",
       "\n",
       "[2000 rows x 5 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "desirable-child",
   "metadata": {},
   "source": [
    "In addition to this meta information added, the neighborhood graph constructed during over-clustering is also stored in the `adata` \n",
    "(If a pre-calculated neighborhood graph is already present in the `AnnData`, this graph construction step will be skipped).  \n",
    "This graph can be used to derive the cell embeddings, such as the UMAP coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "serious-finnish",
   "metadata": {},
   "outputs": [],
   "source": [
    "# If the UMAP or any cell embeddings are already available in the `AnnData`, skip this command.\n",
    "sc.tl.umap(adata)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "velvet-standing",
   "metadata": {},
   "source": [
    "Visualise the prediction results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "convinced-cliff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1483.92x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(adata, color = ['cell_type', 'predicted_labels', 'majority_voting'], legend_loc = 'on data')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fatty-season",
   "metadata": {},
   "source": [
    "Actually, you may not need to explicitly convert `predictions` output by `celltypist.annotate` into an `AnnData` as above. A more useful way is to use the visualisation function `celltypist.dotplot`, which quantitatively compares the CellTypist prediction result (e.g. `majority_voting` here) with the cell types pre-defined in the `AnnData` (here `cell_type`). You can also change the value of `use_as_prediction` to `predicted_labels` to compare the raw prediction result with the pre-defined cell types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fifth-inventory",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 374.4x324 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "celltypist.dotplot(predictions, use_as_reference = 'cell_type', use_as_prediction = 'majority_voting')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "broken-visitor",
   "metadata": {},
   "source": [
    "For each pre-defined cell type (each column from the dot plot), this plot shows how it can be 'decomposed' into different cell types predicted by CellTypist (rows)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "controversial-writer",
   "metadata": {},
   "source": [
    "## Assign cell type labels using a custom model\n",
    "In this section, we show the procedure of generating a custom model and transferring labels from the model to the query data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "polished-radical",
   "metadata": {
    "tags": []
   },
   "source": [
    "Use previously downloaded dataset of 2,000 immune cells as the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fatty-dating",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_2000 = sc.read('celltypist_demo_folder/demo_2000_cells.h5ad', backup_url = 'https://celltypist.cog.sanger.ac.uk/Notebook_demo_data/demo_2000_cells.h5ad')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "corrected-provision",
   "metadata": {},
   "source": [
    "Download another scRNA-seq dataset of 400 immune cells as a query."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "periodic-castle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "826ac407319f454fb87fba75116f0495",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0.00/7.62M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adata_400 = sc.read('celltypist_demo_folder/demo_400_cells.h5ad', backup_url = 'https://celltypist.cog.sanger.ac.uk/Notebook_demo_data/demo_400_cells.h5ad')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pacific-thompson",
   "metadata": {
    "tags": []
   },
   "source": [
    "Derive a custom model by training the data using the `celltypist.train` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "favorite-clerk",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "🍳 Preparing data before training\n",
      "✂️ 2749 non-expressed genes are filtered out\n",
      "⚖️ Scaling input data\n",
      "🏋️ Training data using logistic regression\n",
      "✅ Model training done!\n"
     ]
    }
   ],
   "source": [
    "# The `cell_type` in `adata_2000.obs` will be used as cell type labels for training.\n",
    "new_model = celltypist.train(adata_2000, labels = 'cell_type')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "negative-finish",
   "metadata": {},
   "source": [
    "By default, data is trained using a traditional logistic regression classifier. This classifier is well suited to datasets of small or intermediate sizes (as an empirical estimate, <= 100k cells), and usually leads to an unbiased probability range with less parameter tuning. Among the training parameters, three important ones are `solver` which (if not specified by the user) is selected based on the size of the input data by CellTypist, `C` which sets the inverse of L2 regularisation strength, and `max_iter` which controls the maximum number of iterations before reaching the minimum of the cost function. Other (hyper)parameters from [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) are also applicable in the `train` function."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "covered-transsexual",
   "metadata": {},
   "source": [
    "When the dimensions of the input data are large, training may take longer time even with CPU parallelisation (achieved by the `n_jobs` argument). To reduce the training time as well as to add some randomness to the classifier's solution, a stochastic gradient descent (SGD) logistic regression classifier can be enabled by `use_SGD = True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "initial-signal",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For illustration purpose; below is not run for this small training data.\n",
    "#new_model = celltypist.train(adata_2000, labels = 'cell_type', use_SGD = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "athletic-experience",
   "metadata": {},
   "source": [
    "A logistic regression classifier with SGD learning reduces the training burden dramatically and has a comparable performance versus a traditional logistic regression classifier. A minor caveat is that more careful model parameter tuning may be needed if you want to utilise the probability values from the model for scoring cell types in the prediction step (the selection of the most likely cell type for each query cell in this notebook is not influenced however). Among the training parameters, two important ones are `alpha` which sets the L2 regularisation strength and `max_iter` which controls the maximum number of iterations. Other (hyper)parameters from [SGDClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html) are also applicable in the `train` function."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "local-strain",
   "metadata": {},
   "source": [
    "When the training data contains a huge number of cells (for example >500k cells) or more randomness in selecting cells for training is needed, you may consider using the mini-batch version of the SGD logistic regression classifier by specifying `use_SGD = True` and `mini_batch = True`. As a result, in each epoch (default to 10 epochs, `epochs = 10`), cells are binned into equal-sized (the size is default to 1000, `batch_size = 1000`) random batches, and are trained in a batch-by-batch manner (default to 100 batches, `batch_number = 100`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "great-tourist",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# For illustration purpose; below is not run for this small training data.\n",
    "#new_model = celltypist.train(adata_2000, labels = 'cell_type', use_SGD = True, mini_batch = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adverse-diversity",
   "metadata": {},
   "source": [
    "This custom model can be manipulated as with other CellTypist built-in models. First, save this model locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "automotive-throw",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the model.\n",
    "new_model.write('celltypist_demo_folder/model_from_immune2000.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "crucial-invasion",
   "metadata": {
    "tags": []
   },
   "source": [
    "You can load this model by `models.Model.load`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ideal-natural",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_model = models.Model.load('celltypist_demo_folder/model_from_immune2000.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "enormous-singing",
   "metadata": {},
   "source": [
    "Next, we use this model to predict the query dataset of 400 immune cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "postal-auditor",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "🔬 Input data has 400 cells and 18950 genes\n",
      "🔗 Matching reference genes in the model\n",
      "🧬 16201 features used for prediction\n",
      "⚖️ Scaling input data\n",
      "🖋️ Predicting labels\n",
      "✅ Prediction done!\n",
      "👀 Can not detect a neighborhood graph, construct one before the over-clustering\n",
      "⛓️ Over-clustering input data with resolution set to 5\n",
      "🗳️ Majority voting the predictions\n",
      "✅ Majority voting done!\n"
     ]
    }
   ],
   "source": [
    "# Not run; predict the identity of each input cell with the new model.\n",
    "#predictions = celltypist.annotate(adata_400, model = new_model, majority_voting = True)\n",
    "# Alternatively, just specify the model path (recommended as this ensures the model is intact every time it is loaded).\n",
    "predictions = celltypist.annotate(adata_400, model = 'celltypist_demo_folder/model_from_immune2000.pkl', majority_voting = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "worse-custom",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = predictions.to_adata()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "divine-summit",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.tl.umap(adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ordinary-charlotte",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABLgAAAEKCAYAAAARn+yPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAACQ8UlEQVR4nOzdd3RURRvH8e/sbnonhQABQu+9CShFioogdlEsKHZRQQEVfRUUbIggdiyAiliQJgoCKr1I7x0CoSSk97a78/6xIRDSk01/PufkkNwy99kF9pc7994ZpbVGCCGEEEIIIYQQQojKylDeBQghhBBCCCGEEEIIURLSwSWEEEIIIYQQQgghKjXp4BJCCCGEEEIIIYQQlZp0cAkhhBBCCCGEEEKISk06uIQQQgghhBBCCCFEpSYdXEIIIYQQQgghhBCiUpMOLlFtKaW0Uqpx5vdzlFKTy7smIYQQFZtSKkQp1T/z+wlKqa/L4Jh9lFJni1JbIbbNysBi1FPsfYUQQuROKfWFUup/Jdg/USnV0J412VNJX58QhWEq7wKEqCyUUn2AH7TWQeVcihBCiApAa/12YbZTSs0BzmqtXyvdioQQQlRWWusnS7i/+6Xvyzt3lFIjgEe11tdeWlbS1ydEYcgdXEIIIYSolpRScqFPCCFEpSZZJsRl0sElqgSlVF2l1EKlVIRSKkop9Unm8keUUoeUUjFKqb+UUvWL2b4bsByonXn7b6JSqrZSKlkp5XvFdp0ya3BQSo1QSm1USn2slIpTSh1WSvW7YlsvpdQ3SqkLSqlzSqnJSiljSd8LIYSo7jIf1XtFKXUw8/N/tlLK+dKjfkqpl5RSYcBspZRBKfWyUupEZn78opSqcUVbDyilTmeue/Wq40xUSv1wxc/XKqU2KaVilVKhmTnwODAcGJ+ZHb9nbltbKfVbZmacUko9d0U7LpmPzscopQ4CXYrxHnRVSm3OrOWCUuoTpZTjVZsNUkqdVEpFKqWmKqUMV+xfqPxUSg3KfJ8TMrNsbFFrFUKIyiwzc8YppfYqpZIyf7+vqZRanvnZuFop5ZO57a9KqbDMc4N1SqlWV7STbcgUpdRjSqnjSqlopdRSpVTtK9ZppdQzSqljwLErljXOLXcy6/vtqro/VkrNyOd1DVNKbb9q2Ril1NLM772UUt9l5thppdRrmZnaAvgC6J55/NirX98VefyiUupiZk49fMVxfDPrjldKbcs8T9pQxL8aUQ1JB5eo9DI7hZYBp4FgoA7wk1LqVmACcDvgD6wH5hfnGFrrJOAm4LzW2j3z6zywBrj7ik3vB37SWmdk/twNOAn4AW8AC684cZoLmIHGQAdgIPBoceoTQgiRw3DgBqAR0BS49JhGIFADqA88DjwH3Ar0BmoDMcCnAEqplsDnwAOZ63yBXB9TV0rVw3Yh5GNsmdMe2K21ngXMA97PzI4hmR1JvwN7sGVWP2C0UuqGzObeyKy7UeZreKgYr98CjMGWP90zj/H0VdvcBnQGOgJDgUcyX8utFD4/vwGe0Fp7AK2Bf4pRqxBCVHZ3AAOw5c0QbHkwAdtnsAFb1pC5vAkQAOzElg85KKWuB97Bdp5RC9t5zk9XbXYrtnONllcuzC13gB+AG5VS3pntm4B7gO/zeU1LgWZKqSZXLLsP+DHz+48BL6Ahtgx9EHhYa30IeBLYnHl87zzaD8zcvw4wEvj0UkcgthxOytzmIYqXg6Iakg4uURV0xXbiMU5rnaS1TtVabwCeAN7RWh/SWpuBt4H2eV2FLqa52Dq1LnW03Uv2oLgIzNBaZ2itfwaOADcrpWpi6zAbnVnzRWA6MMyOtQkhRHX2idY6VGsdDUzB9vkMYAXe0Fqnaa1TsGXFq1rrs1rrNGAicGfmL/93Asu01usy1/0vc//cDAdWa63nZ37mR2mtd+exbRfAX2v9ptY6XWt9EviKyxlwNzBFax2ttQ4FZhb1xWutd2itt2itzVrrEOBLbCcgV3ov8xhngBlcfo+Kkp8ZQEullKfWOkZrvbOotQohRBXwsdY6XGt9DttFga1a612Z2bEI28VstNbfaq0Trsibdkopr1zaGw58q7XembntK9juiAq+Ypt3Mj/DUwoqTmt9AVgH3JW56EYgUmu9I599koElZGZDZkdXc2Bp5nnPPcArma8nBJiG7YJQYWUAb2Zm5p9AIrYONSO2DsM3tNbJWuuD2M65hCiQdHCJqqAucDrzl/Ar1Qc+ynw8IxaIBhS2qwT2sgTbL/YNsV21idNa/3fF+nNaa33Fz6exdcbVBxyAC1fU9yW2qzlCCCFKLvSK7y999gJEaK1Tr1hXH1h0xWfxIWx3P9XM3Cerncy7eaPyOF5d4EQha6uP7ZH32CuOOyHzmFx93Mz6i0Qp1VQptSzzUZh4bJ1Ufldtltd7VJT8vAMYBJxWSq1VSnUvaq1CCFEFhF/xfUouP7srpYxKqXeV7ZH4eCAkc/3Vn81g+zzO+uzXWidiy58rP4dDr96pAFkX5jP/zO/urUt+5PLFj/uAxZkdX36AI9nz6TRFO8+Kuur8LRlwx3bnsInsr6+or1VUU9LBJaqCUKCeyjnAYii2xya8r/hy0VpvKuZxdI4FtpOkX7BdZXmAnEFRRymlrvi5HnA+s7Y0wO+K2jy11q0QQghhD3Wv+P7SZy/k/CwPBW66KiucM6/CX7iyHaWUK7bHFHMTiu2RwtzkdsxTVx3TQ2s9KHN9tuNm1l9UnwOHgSZaa09sHWjqqm3yeo8KnZ9a621a66HYLtAsxpaJQgghcroP2+Pg/bE9mhecufzqz2awfR5n3TWrbOMB+wLnrtgmx7lJAesWA22VUq2BweTxeORVVgJ+Sqn22Dq6Lj2eGIntDqwr7+ytd0V9+dVWkAhsw7hcOSRA3Ty2FSIb6eASVcF/2E4G3lVKuSnbQMI9sQ1u+MqlwRszB0K8K7+GChAO+OZyG/F3wAjgFmzPt18pAHhO2QadvwtoAfyZeZvwSmCaUsozc0DGRkqpqx8fEUIIUTzPKKWCMsc9nAD8nMd2XwBTLj1+p5TyV0oNzVy3ABisbIPHOwJvkvfvTvOA/kqpu5VSpswBcttnrgvHNkbJJf8B8co22L1L5lX91kqpS4PJ/4Itv3yUUkHAs0V/+XgA8UCiUqo58FQu24zLPEZd4Hkuv0eFyk+llKNSarhSyitz7Ml4bHe/CSGEyMkD2wXuKMAV2521efkReFgp1V4p5ZS57dbMRwEL4+rcuXRhfkFm2/9lPp6er8w7rBYAU7GNX7kqc7kFW1ZNUUp5ZGboC1w+FwoHglTOyU0KlNn2QmCiUso1M8MeLGo7onqSDi5R6WV+CA7BNlj7GeAscI/WehHwHrYB5+OB/djGvSrucQ5jG2T3ZOZjG7Uzl2/ENibLzlxCZyu2gSQjsY0Bc6fW+tLjLQ9iu7X3ILZBjRdgG0RSCCFEyf2I7ULCycyvyXls9xG2gXRXKqUSgC3YBu1Fa30AeCazrQvYPqvP5tZI5onCIOBFbI/07QbaZa7+Btvj7LFKqcVX5FZ74BS2jPga2xV9gEnYHvU4lfkaCvMYydXGYrtbIAHb+F65dfAtAXZk1vpHZp0UMT8fAEIyt3uSy4+/CCGEyO47bJ/t57D9/r8lrw211n9jG/fxN2z504iijdWbLXeuWD4XaEPRcuVHbHed/XrVI4XPYhsI/iSwIXO7bzPX/QMcAMKUUpFFONYlo7BlYlhmrfOxdQ4KkS+VfXggIURxKKX+AX7UWn99xbIRwKNa62vLrTAhhKiGlFIh2D5/V5d3LUIIIURhKKW+A45rrd8sxWPUw/b4eqDWOr60jmNvSqn3sNUssymKfMkdXEKUUOYjJR3J+/EXIYQQQgghhMhV5ljCzbDduVtaxzBge4zwp4reuaWUaq6UaqtsugIjsc1GKUS+pINLiCsopSYopRJz+Vqex/ZzgdXAaK11QtlWK4QQojpRStXLI6MSM6/KCyGEqJzCgFhsjyTaXeYg9fHYZn1/46p1eeXKdaVRSyF5YBuHKwnbWF/TsD1WL0S+5BFFIYQQQgghhBBCCFGpyR1cQgghhBBCCCGEEKJSkw4uIYQQQgghhBBCCFGpmcq7gIL4+fnp4ODg8i5DCCEqlB07dkRqrf3Lu46qQrJGCCFykqyxP8kbIYTIyV55U+E7uIKDg9m+fXt5lyGEEBWKUup0eddQlUjWCCFETpI19id5I4QQOdkrb+QRRSGEEEIIIYQQQghRqUkHlxBCCCGEEEIIIYSo1KSDSwghhBBCCCGEEEJUatLBJYSoupKj4cIeSAgr70qEEEJUVVYrhB+0fVmt5V2NEEKIqiohzHZukxxd3pVUWBV+kHkhhCiy5GhY/hIcWATWjMvLlQnq94ABb0KtdmCQPn4hhBAlsPM7WDcVYs9kX+5VF3qMgrbDwMW7XEoTQghRRUSfhOUvw7G/si83OkOT/tB/Evg1Lp/aKhjp4BJCVC2nN8OPd0NafM512gwh6+CrPuBdD3qNg44PlnmJQgghKrm0RPj1YTi+Mvf1caG2Cy1/vQqt74Qb3gY337KtUQghROW37zdY/CRY0nOus6TC4WW2r5qt4fr/QbMby77GCkQ6uIQQVcexVTB/GFjNBW8bewaWPgur3gCjIwR1hmuehuCepV+nEEKIyis9CeYOgfM7C97Waoa9P8HBJeDkAe4B0G4YdB4Jjq6lX6sQQojKa/u3sGxM4bYN3w/z7wFXP9u5TcPe0H0UBLYu3RorGOngEkJUbqnxsOsHOPwHhP5XuM6tK6VkPsN+eJmtjSEzoNMIe1cphBCisju1DnbMhdCttju0isKcYvtKuggrX4ODS+HBJdLJJYQQIruEcNgxG47/DWe3FX3/5Ejbn3vmw/6FcO+P0Li/fWuswKSDSwhRecWdgzk3Q8wpOzWobY+UtLgFXGvYqU0hhBCV3srXYNPH9mvv7H+w9XO47kX7tSmEEKJyu7AXvht6+QJ8SVnSYOlzMHofGIz2abOCkxGWhRBFFhISglIq25e3t3eR2xk1ahRKKdasWZPvdufPn2fixIksXrw4a9nEiRNR3kEs2Hi0SMccsTgFNSme7ecthMRaUZPiGfxj8uUNzKmwb0GO/YKDg3F3dy/SsfKyZs0alFKMGjUKgD59+qCUIjIy0i7tCyFEVVBhskYpFnz9YZGOWWDWAOyal2M/yRohhCh7FSZvardjwY7wIh2zwLyJPwcn/smxX1XNG7mDSwhRbB06dGD8+PEAODo6ltpxzp8/z6RJk3jooYe49dZbbQtDi3HL7lX8XRXz73ChjofKviIxrMRtCyGEsI9yzRo7TMWeZ9YkSNYIIURFUm55Y7XC0TwmLSmCvM9titZpVpnJHVxCiGLz9/enf//+9O/fn379+gEwZ84clFI89NBDdOjQAR8fHz766CMAtNa88MIL+Pj40Lt3b86ePZutvSVLltCmTRvc3Nxo3bo1S5YsAaBLly4AzJ07F6UUc16+C06sBmDjGQvNP0nEf2oCvx7IACDdohm7MpU6Hybg/W48d/2aTESSNUf9Ecmae39L4b2NtllJ3t+YRu1pCTje+CZBQUFMmjSpUO/Df//9R69evfDw8CAgIICFCxcCsGzZMtq1a4ebmxvt2rVj9erVBbZ15MgRunXrhouLCz4+PvTq1atQNQghRFVVblnzyfuw63uglLLmjTDJGiGEqEDKLW+eux7O7wBKKW+6PVxt8kY6uIQQxbZy5Ur8/f3x9/dn6NCh2db99ddfPProoyilePnll0lPT2fp0qVMnz6dtm3bcvfdd/PPP5dvlz1y5Ah33XUXGRkZTJ8+HbPZzF133cWRI0eYMmUKAL169WL+N5/RO21V1n7Lj5t5qrMDcamal/9OBeCd9elM25zOkKYmRl/jyPJjZp76I7XA11PX08D/ersw48MPaNu2LRMnTmTjxo357hMdHc2gQYPYvXs3b7zxBi+//DIGg4GjR49yxx134OLiwmuvvYaTkxO33XYbFy5cyLe9zz77jP/++4+3336bd955h3r16hVYtxBCVGXlkjXz59Nb/QcZtsc87J41vZyYMfpOyRohhKhAyiVvPn6L3sYdWfvZPW/6eTNjxkfVJm/kEUUhRLF169aNyZMnA+Dj45Nt3SOPPMIzzzzD77//zl9//UV4eHjW8+ivv/46/fr1Y8uWLfzwww8ArFq1ioyMDF588UUee+wxlFI8/vjjrF69moEDB/Lqq6/SoEEDhrWwwhmddZwXujvyeCdHPt+ewbFo25WMZcdsVzu+3JGRtd3KEwXPrngxycqkdenE/HF5Ot59+/bRs2fPPPfZvHkzUVFRjB07lrFjx2Yt//TTT0lPT2fr1q1s3bo12/Y1auQ9gH2TJk1s9a5cSZcuXXj++ecLrFsIIaqycsmaO4bCO6OyjmP3rFmbRsyfv2Ytk6wRQojyVy550yAGIi/fd2T/c5s4YpY8k7WsqueNdHAJIYrNz8+P/v1zn3b20gedyWT7mLFYLDm20VrnWKaUyn9Zalz247jY1pkMYNWX2rX9vOxeV4yZeWHNeahsktI1L6xMo46H4ovxw9mTUZ+3336b1NSCr47k5tJrGz9+PAMGDMha3qJFC44dO5bnfqNGjaJFixasXbuWJUuWMGXKFA4ePEizZs2KVYcQQlR25ZI1GSlgvXwiUSpZc7sfe4Ifk6wRQogKosqe27zzKnsu6mqRN9LBJYQotvPnz/PTTz9l/XzHHXfku33fvn2ZMWMGb775JocPH2bp0qVZ6wYMGICDgwPTpk1Da8306dNxcHCgf//+ODg4ALBr1y7mt/ZnQHLOZ86vNKSpAzsupDF3Tzr9G5o4GGHlVKyVgY3y/sjTgALSLBBzZCPLDu4r+A0AevToga+vL19++SU1a9bEZDIRHBzMwIEDcXR0ZOHChTRu3JjY2Fh++eUXfv3113zb++KLL4iMjKRx48Y0btyYvXv3Eh4eLicdQohqq1yyZvEKBhhrAyfzPE6JsyYugWWLc87amxvJGiGEKH3lkjcNOpT+uc3+VSz7r3AdW5U9b2QMLiFEse3atYt777036yspKSnf7YcMGcKYMWPYs2cP8+fPzxq8EaBZs2b8+uuvmEwmnn/+eQwGA7/88gvNmjWjYcOG3HfffRw9epT7xn3A4bSa+R7nlescGdfDkfVnLIz6M5Xlx830rm/Mdx93R8X7A5xIM2tm/n2GgQMHFuo98PHx4c8//6Rdu3ZMnDiRt99+G6vVStOmTVm4cCHu7u48//zzTJ8+nUaNGuW43flqjo6OzJ49m8cee4y1a9fyzDPP5HsbsRBCVHXlkjXDh3PY78Z8j1PirPkvnYEdggv1HkjWCCFE6SuXvJk4l8NxLvkep8R5s2xvtTm3UbndRleRdO7cWW/fvr28yxBCVCQXD8MPd0D82YK3LQ5HD5hQSm3biVJqh9a6c3nXUVVI1gghcrBa4ffnsmZSLBVDP4MOw0uv/RKSrLE/yRshRA6n1sPPw3M8rmg3gW3hyfWl07ad2Ctv5A4uIUTlE9AcntsJ/V4vnfZb3Vo67QohhKg8DAYY+gk8uRFc/ezfvskZmt1k/3aFEEJULg2ugzEHoNMjpdN+NTq3kQ4uIUTlZHKC616EWz7G9oS5nbjUsLUrhBBCAAS2hmd3gFdd+7Z73YvgmvfMU0IIIaoRJw8YMh16jil426Ko0Qg6l1LHWQUkHVxCiMqt44Mw/hS0ug0c3Iq2r8EEju6275URmt0Mj/wFNRrYv04hhBCVl4s3jNkPt80Cj9oU+VdoZ6/L3/s0gMHTofd4e1YohBCiKhgwEZ7fBw36gNGpaPuanGx3B4Ptz3b3wiMrwCX/cbKqEplFUQhR+bn6wF1zbN+b02DFy7BrHljSct9emeDa0dDjWXBwhcQwcPK0ncAIIYQQeWl3j+0L4MgKWPESxITkvX2dznDzB1C7AyReBEs6eNaBXKaNF0IIIQDwqQcPLbF9nxwNvz8Ph5eBzmO2RQc329AtnR6y/Zx4EVx9wcm9bOqtQKSDSwhRtZicbFfG+74GIRtg51zbwI3WdNv62h3hpvegbtfL+3jXK59ahRBCVF7NboQmA+Hsf3D4D9j/G8Sfs61z9oZuT0Dvl21jeQG4B5RbqUIIISop1xpwz/cQG2o7p9k5F0K3AhpQ0KCX7dzHt9HlfXzql1e15U46uIQQVZObL7QaavtKioKo47aA8GtS3pUJIYSoKgwGqHeN7av/JAjbC5YM27hdDvlP+y6EEEIUmndd6HCf7Sv+PMSeAY9a1bozKzfSwSWEqPrcfG1fQgghRGkxGKB2+/KuQgghRFXnWdv2JXKQQeaFEEIIIYQQQgghRKUmHVxCCCGEEEIIIYQQolKTDi4hhBBCCCGEEEIIUalJB5cQQpShjLAwMsLDy7sMIYQQVZglMZH0s2expqaWdylCCCGqKG21knHuHObIyPIuJYsMMi+EEKXImpZG8tatJG3aROLataSfCgHAoW5d/F8Yg9dNN5VvgUIIIaqE1CNHSNm1i/gVf5G8bRtYLCgnJzxvvpnAVydgcHMr7xKFEEJUcpaEBJL/+4+Ef9eQtGED5rAwABybNSPwtVdx69KlXOuTDi4hhCgl0fPmEfHRTKzx8TnWZYSGcn7MC6Rs207g6/8rh+qEEEJUBelnznB+/Euk7N6dY51OSyNu4UIS//2XRiuWY/TyKvsChRBCVHpaayJmfETU3LmQy93B6UeOcOaBBwmY8Aq+Dz5YDhXayCOKQghRCmIXLSb8rcm5dm5dKebHH0lct66MqhJCCFGVWBISOHX/A7l2bmXbLiaGM4+MLJuihBBCVDkRM2cS9eWXuXZuXeniu++Rdvx4GVWVk9zBJYQQdmBNTydu8WLily3DmmEmdd++Qu8bPvUD3Hv1KsXqhBBCVBUphw8TPesr0s+dxRIdg/XixULtl3rgAKlHjuDcrFkpVyiEEKKysyQmEfPjPBLXrkVbrKQWcCEli9VKxMyPCZr5UanWlxfp4BJCiGKwpqQQt2wZKdt3kBERQcr27ej09GK1lX7sGCkHD+LSsqWdqxRCCFHZpezeTdyyP8iIjCR1317M584Xu63wd9+j/uxv7VidEEKIqsASG0vsosWkHT5E+rnzpOzaBRZLsdpK+PtvzJGRmPz87FxlwaSDSwghiijt2DHOjHwUcyGvmhfGhQmv0nDxIru1J4QQonLTVisXJrxK3OLFdmszefNmknfswLVTJ7u1KYQQonJL2rKFs6OexZqYaJ8GLRbCJk8haMZ0+7RXBNLBJUQVkZRmZtGuc+wJjcXNyURDPzeOXkwgMdVMmyBv7uwUhJeLQ3mXWelpq5XQUaPs2rkFkHb4MCl79+LStq1d2xVCCHvbHhLNsr0XSEoz0yjAndQMC6cik/BxdeSOjkG0CZKBzO0h5od5du3cymr3p5+lg0sIUeFFJ6Xz6/ZQjl1MxMfVgSBvVw5ciMNs0VzT0Jdb2tfG2cFY3mVWepaEBPt2bmVKWL26XO7ikg4uISqpyMQ0luw+T2RiGs4mA99tDiEqKSPXbRfvPs/Mv4/x7YgudKrvU8aVlo2QkBAaNGjAzTffzLJlyzh27BjXXnstaWlprFmzhvbt2xe77fHjxzNr1izi4uL4eOxY6h09xtjz5zmZnoafycS/jRrb5TWknz5TqA6uOXPmAHRSSo3VWn+glAoB/LTW7nYpRAghMlmtmjVHL7LjdAxORgM7zsSy9mhEntvP2RTCA9fU561bW5dhlWWnrLLmq6++ovY33zD61Cn7Z82Z04XaTrJGCFGWQqOTWbb3AgmpGSgF36w/RarZmuu2C3ed46O/jzHv0W4E+7mVcaVlo6zyZsYjI2kQFWX/cxuzmYxz5wrVwWXPvJEOLiEqibMxyaw5EkFMcjrrjkawPSQGXYT941IyeGzuNja90q/KX+04d+4cAwcOJCEhgZUrV5YoAJKSkpg6dSr16tXjk08+odnp03weF8ex9DTu9/bhGjfXIrVn1hqTUrmuMwUEFLtOIYSwh3SzlX8OhxMancKJiESW7jlPcnrRxuD4fstp2tTx4u4udUupyoqhNLPmmk6deHv8S6WSNQ6SNUKICuBIWAJbTkYRk5TO8v1hHAlPKNL+52JTeGreTpY/f10pVVhxlGbeNNqwka+q0LmNocyPKIQokgyLlZd/20uv9//ltcX7mbbyKNuK2Ll1SXRyBsv2XrB7jRVJdHQ0AwcO5Ny5cyxYsIBrr70WgD59+qCUIjIyksjISJRS9OnTB4CJEyeilOL555+nadOm1KpVi3nz5gHQqlUrAM6cOcMDDzzAhi1bmB0TDcAPsTHMjbZ9PysqigEnT9D56FEeCw0lNHPA+U8iI2h55DCvXrjAwJMn+CAi56ON65MSuTP0DP59+xAUFMTmzZsB+Pbbb2nWrBlubm706NGDnTt3Fvj6lVI9lVJ7lVKpSqkIpdT84r+bQojqZO3RCHq8+zdP/rCTKX8e4qdtoUXu3LpkzqZTdq6uYintrFm3bl3pZE3IKZp+9ZVkjRCi3CSkZjByzjZumLGON5YeYMbfx4rcuXXJoQvxbAuJtnOFFUupn9vs2lUqeXPX+fN4NWpU5nkjHVxCVHBvLTvIT9tCsRanRysXhy/E2aehCmrz5s0cPHiQL7/8kkGDBhVp37///puxY8diMBh49NFHCQ8P5+233wagRYsWzHrsMWrt3UdPV9ut0E/5+vKUrx+L4+KYERlBW2dnHvWtwZG0VF44n32Wq03JSYysUYPebtnvtA1JT+fZc+eIBKZOncpTTz2FxWJhzZo1jBw5kuDgYF577TWioqK45ZZbSE1NLehljAcaAs8DbwKRRXoThBDV0vGLCTw6dxuRicWbDfZqxT1ZqSxKM2u+nzkTz1mzSiVrLprNvP/++5I1Qohy8/S8nfx92H5j2R4Jk7zJS0F589kdd1A3LKxU8iYCXS7nNvKIohAVWFhcCt9vKdxYGYXlVMUfTzQYDFitVn755Rfuv/9+HBwKP7D+mDFjGDlyJKGhoUyePJmtW7cycOBAAPxcXLh23XpwcaG+owMbk6GbqytdXd0Yfe4cAMsTElieYAvZSIuF2Cum1n2kRg3u8c45/tmmpCTStealp5/mmWeeyVo+btw4AFauXMnKlSuzlh88eLCgl3EMGAwMBHYCnxb6DRBCVFuvLNxHhsVOV1IAB2PVvoZaWlkTEBBAj3/+JS0puVSy5rlu3Rj13HNZyyVrhBBlacvJKNYfs29/uI+ro13bq2hKK29qKAN99h8otXObV196qVzObaSDS4gKbMbqY2j7nW8AYDLk/ox0VTFw4EBcXFxYtGgRI0aM4IcffkAphdFo69gzm80kFjBLiM7lTc8IDwd3j3z3e79WLWoYbR+rGnC54nn0AFP+H7dOwcG51jBt2jTaZg48b7VaadCgAXv37s2vqfHAOqAHMBJ4RSkVpLWOzbcAIUS1FZeSwbaQGPs2qjVaa1Qe43JUdqWVNdbERNLCwvPdryRZ496rV641SNYIIcrCtJVH7N6mo6lq5swlpZU35ovh4Jv/APAlyRujt3euNZR23kgHlxAV2PpjtpmqzHHhnPtipG2h0QGjqxdOdVvhfd0DOHgHApCwcxnxO5ZhjgvD6OKJS+Ou+N4wCnP8RSKXTiUt7BhYzHwZ+jpjBkwqr5dU6oxGI/Pnz+eGG27gxx9/xNfXl5kzZxKc2YE0a9YsjhzJPVynT5+OxWJh9uzZuLi40K1bt6x1OiU1zw6uPu7urExMYElcPDd5enA2I4P/kpP5oV79Auvt4eaGk6MjkydPBiA+Pp7rrruOwYMHM23aNObPn4+3tzcXLlzg+++/5/DhwwU1OQFIAw4AoUADwBOILbAYIUS1tHyfbWxGe2aN39CXWXesC72b+pfXyypVpZU11pSUPI9Z0qxxNBh4f84cHIOCJGuEEGXOatXsOmO7mGLPvHkrbgoDPp9QXi+r1FXGvHFyciq3c5uqff+4EJVcfEpGtp8dazbC94ZncK7fluSDawn/YRyWpFhi188jetUXoK349B2JR+ehZFwMAUCbMzB5B+IcZJuyPSopneR0c1m/lDLl5OTE0qVLad++PR9//DFvvvkmY8eOpVmzZsycOZPAwMBc9xs4cCAffPABFouFr7/+mpo1a15e6ZD39YBbvbx4wc+f0xnpvBkezh/x8XR2KdwMJK0GDmTR4sXUrl2bcePG8emnn2I0GunTpw+zZ88mMTGRZ555hlmzZtGjR4/CNGkFngO+AZoCb2itzxSqGCFEtRR11bhb9sgagN1n7HxXWAVTGlmj8rkiXpKsCXZ1ZcHcuZI1Qohyk26xYrZmX2aPvDkRkVTGr6TslU7e5P2oY0nypuOTT7Bo0aJyyxuV2+1qFUnnzp319u3by7sMIcpF76n/cjoqOesqh0ujLgTc+QYAUSs+JnHPX3j1vI/4rQvQ2kqdx2dh8rRNx6q1FaUu92HHbphH3Mb5+A19mQu/TcFUxcdHKYqJEycyadIkfv31V+68885ct4lduIgLE+x7dcjo70fT9euLta9SaofWurNdC6rGJGtEdfbnvgs8PW+n3bPm5ltuY84jXcvlNVVEhckaa3o6x/v0xRJt31nBan/0EV43DCzyfpI19id5I6qz1m+sIDHNYte88R/6MhcXv1Mur6eiKkzeXJwxg6gvvrTrcZ1ataThb78Va1975Y2c4QpRgQ1pWyvPdS4NOwGQGrofbU7HwbduVgAA2QLgagcvxNuvyGrCa8hgXAt3laHQagy/367tCSFEcdzQsibGPMZnLEnWRCam2bfQasDg6Ejg66+DHccuM9WsiWe/6+3WnhBCFFf3RnmP+VTcvNFATJJ9ZgCuTnwfeQSHhg3t2qbfyJF2ba84pINLiApszIBmeDjn8biCvvqbwv8yHCUhkM3EiRPRWud5hQNAOTjgc/dddjumQ+3aeN9zt93aE0KI4jIaDTzcIzj3lSXIGkeT/Jp5pcJkDYDHwAEY/fIf+Lco/J97Nt9HH4UQoqy8e3ubPC+olCRvYq8a1qW6K0zeGD098Roy2G7HdG7TBo8BA+zWXnHJbx5CVGBGg+Knx6/JdV3KqZ0AONdtgzI5khF1BnP85Wl3tbbmuh9AkwB3+xZaTSRvs8MjBQYD7v37UX/eD5h8ck6tK4QQ5eGVQS1o6O+WY3lJsqZZYP4zz4rcZZw5gyUiosTtONSvR+3338P7jjvsUJUQQpScr7sTX9zfMdd1xc0bo0FRy8vZ/sVWAym7dpW8EUdHvG6/nXrffI1yyHtcr7Iil3OEqOBa1fbilna1+RywJEaTuG81qWf2k7T/b4xuPnh0uAnQxG2cT/jPr+HZaTDanEHy0c0E3v8+1vQUkg6tIz38BABpp/ewfME8HnvssXJ9XZVSEcctU16euDRvgdHTE7frrsW5RQscatXCZMcr80IIYQ9Gg+LtW9tw3Vv2yZrU03s4tcEdbm9bvi+sMirG3VbO7dph9PTEuUULXLt2xSGwJo6NGqHs+KijEELYw4CWgXSu78M57JM3GaF7+WHubB599NFyfV2VkTIYi7S9KTAQx+BgTL6+uPXqhVOTxjgGBWH09CylCotOBpkXohLYsvcw3du1sP1gNGF09ca5bmu8ez2Aycs2G0b8jt9J2PkH5tgwDM7uuDbuiu9Nz2WfhvcKFf3/fkWUtGULZ0Y8XKhtnVq0oP53czF6lM4dDDLwr31J1ggBISEhNGjQwPaDZE25OjnkFtKOHSt4Q6ORoJkf4dGvX6nUIVljf5I3QsCfm/Zyc892th8kb8pNzM+/EPbGG4Xa1q13b+p++kmpPfJur7yRDi4hKom+H/zLqcjkErfTyN+Nv1/sU/KCqqnTD40geevWPNcrV1dqvvwy3rcORTk6llodctJhX5I1Qtgs3HmWF37ZY5e2Vr/Qm8bySHyxxK9cybnnR0M+v6e7Xd+XwAmv4hhUp9TqkKyxP8kbIWxav/EXiWnmErfTr3kA34zoYoeKqh9rSgqn7riT9JMn89zGWKMGtd6chPv116MMpTfClb3yRh5RFKIS2HIyyi6dWwADWta0SzvVVd3PPiVs8hTily1DZ9gGtDS4u+PcqhU+9w7Do39/GcxXCFFpzVqX9y+5ReHhZJTOrRLwHDgQpn3AxQ+nk3H2rG2hyYRDndp43ngTPvcOwyEwsHyLFEKIYvple6hdOrcAhravbZd2qiODiwv15swmbOIkEtesAattnDODlxeuHTviM3w4bj17VKrH3eUsTIhK4NsNpwDbnCLa2xHtZEAlmTEkFj0YujX0tXN11YvBzY3a77xNwLixpB0/bhvzpHnz8i5LCCFKbNOJSA6HJQCgnQxYvR3BojFEp6HyHks+V0E+rqVQYfXiOWgQHjfeSOqBA+jUVJxatMDoLp2GQojK79K5TUkpoG2Qt13aqq4cAgKo+9mnZFy4QPqZUEwB/jhdGq6gEpIOrkLQ2sLp018RenY26emRgBGj0QVn5zoE+N9InaD7cHKUQaNF6dkdGovVx5GMVj5ot8v/bVVMGg77YjCkWArd1nebQujbLKA0yqxWTDVqYOratbzLEFVMfPxejh6bQlzcTkBjMDjhYPLG06sDdYMewMenW3mXKKqwPaFxaKMio6U31kAXuDSVe7oF07F4TGcLfyfx4bAETkUm0cAv58yMovCUwYBLmzblXYaoYiyWFE6enMH58z9jtiSglAmjwRUX12BqBd5KrVq3YzLJLKiidKRmWLIuppSUBuZsCmHiLa3s0l515lCrFg61apV3GSVWeg9RVhHJyaFs3jKAEyenZnZuAViwWBJJSjrCqZCP2Ly5H3Fxu8uzTFHFmbwcSe/om61zC0D7OJHR2Q9tLPxto2uPRmC1Vuyx94SobqzWdI4ff59t228nLm47YAU0VmsqaelhREQsZ+eu+zhydGI5VyqqMjcnIxltfbDWdr3cuQXgaMTcygdLLZdCt6WBNUcu2r9IIUSJxMfvZeOmXpwJ/RqzJfOOTW3GbIknIWEvR4+9yZatN5KSElrOlYqqysFowGSw3yNvfx8Kt1tbovKrsndwaa2xWBIxGFwwGIr2MrW2kpR0ktOnPyMsfCm2X9PyZrEksm/fM/TsuQ6lijbVphCF4dbCBwwWGiZa6B5pu1trk5+RU+5GtKsJS21XTKFJhWqrgs8rIUSlY7GkARaMxqI/kpWeHklU1DqOHXuXDHNUgdufPfs9Pt7XEBBwYzEqFSJ/wQ28sabG4mzW9As345dmJdTVwNoAExaDwtzQA+OFlEK3J3kjhP1obcViScJodEOpot2jYLVmkJh4hBMnPyA6en2B26elhXHg4Dg6d/qpuOUKkSejQeHl4kBUUjqtMNIWIxnAOjK4WMB5d25SM4r4DL2o0qpcB5fVaubMma85EzqHjIwIlDLi5taMpk3ewGh0RCkT7u7N8wyG8xcWEHLqU1JSzxTpuGnpYURGrcHfr3SmaRbVW4wJ3tuZQr/wy2NujTkCq2uaeL2NM2Y/JyhkB1dDfzcMdrxqIkR1FRu3g5MnZxATswkAB4ca1Kl9H4G1bsWcEYurazAODj657puSEsrRY5OJjPwH291ahXfu3I/SwSVKxZ60NPqFZfDagVQ8rhji8aKT4pV2zuzxcUA7G1GphXssvk8z/1KqVIjqw2xOIiTkE86em4/FkoBSjnh5taNpk0lYrSkYja64uzfNdV+tNadPf3nFMCuFFxe3jcSkY7i7NbHHyxAiS2qGBUtSBp/gSvsruiNG4cQi0vmItCK11ybIy94likqsynVw7T/wPBERK7J+1tpCYuJBdu66J2uZk1MQDRuMwtevLw4mLwwGBwDOnp3HkaOvF/vYKckhxd5XiPw8fSA5W+fWJf3DzSSa0ni7COcQo65vbMfKhKieoqI3sHv3o0BG1rKMjGhCTn9CyOlPMpc4EBg4mAbBz+Ho6JM1nklaeiQ7dg4jLS2sWMdOSjpRwuqFyJ3PxVSm7E3FdNUF9IA0zYydKdx5rRuJhWwr2NeVhv4yILoQJWGxpLFz1wMkJOzJWqZ1OrGx2/hv26CsZe5uzWjY8AU8vdrj6OCT9UTJ8ePvcCb0m2IfPyU5RDq4RKl4C5dsnVsAJhR34UQUmh9IL3RbYwfm3sErqqcqNQZXdPTGbJ1bl3w8M5L+/U6ye7fttvq0tLMcOvwyGzZ0Y83athw8+BLp6dH8tvBN+vc7ycczbVc4/v47kblzo0lMLNyVSkdHWy/DxIkTUUrl+rVmzZoSv84RI0aglGL79u2EhISglGLw4MElbldUTJaEdPqdzftDftD5DPzCUwFwMhmo6eGU57ad6/twW4cgu9coRHVz7NhkruzcuiR73mQQFraIzVv6snZde7ZsuZGYmK2cDZ3L1q2nip03jk41s76XvBH21PNIYo7OrUs8zHDHibSsu7fq+uQ9HpfJoPjygU6lUaIQ1UpY2MJsnVuXXH1uk5h0hL37nsg8t+nAsePvkZp6jt+XfV6icxunzLyRrBH2ZAhLpkM+99nciSOXBv1xdzLi7eqQ57Z3dKxDy9pyB5e4rErdwRUevqzI+2idzoWwBVwIW4zZnH02h3/+TmTr1mRuuMEDd/f8x9ZSygF//4EA3HnnnTRv3pyEhAQef/xxWrRoweuv2+4Ma9myZZFrFNVb+pkEjPk8weSo4d++rXBo6oOHkwmlFO/8eYg5m0JIM9t2NCq4q3MQE29pXUZVC1F1JSQeJinpWJH3S0o+xs5d9wGOOdYVJW/q1L4763vJG2FPTqcT8n1gdphyYuzEgTibjDiaDOw/F8dTP+wgNObyuFwN/Fz58O72NAv0LP2ChajiwsJ/L/I+VmsSZ87MIjR0Llpn78gqStY4Ogbg6dkWkKwR9pV6Mjbf9X4Y2PNsL7SvMx7ODlitVsb8soc/9l7AnDlRlpNR8XjvRrwwQO7eEtkVeAeXUupFpVSkUmqHUmqOUkorpUZkrvtVKRWjlEpVSh1USt2WuTw4c7t1SqnlSqkEpdRLmW3FKaV2K6WCM7e91OaHSqkwpdQBpVQ3pdR/Sqm4CxcuABAREUGHDh1wd3fH3d2d6667jgMHDmSr9VIHldaazz+L4tahIbww5jwREdkf7dqyOYnHHzvL4JtP8fhjZ9mxIxnIvs3cudFs3WqbDvv+4aEMv882JteoUee4Zcgpbh50iqeePMu+vbZf6oLrP4XR6AxA69atGTZsGLfddhsAAQEBDBs2jGHDhhEQEJDjPZ42bRqNGzfG2dmZVq1akZycTHp6OmPHjqVOnTp4e3tz1113ERERUdBfF19++SV169bFycmJevXqMW3atAL3ERVTRlgSMUuOE/t7wY8jubk44OnsgFK2sbVeGdSCfRNv4IeR3fj6wc5sfbU/797RDmcHmQRBVFzlmTe7du3irbfeAgrOmysvhhQvb7LfkVmUvDEa3ahV646sfSVvRElZ0ywkbj5PxLf7sSbkvCvxSgFuTng6O+Bosv362LqOF+tfup5VY3ox64FOLHmmJ/+O7UuHermPPSdERVCZzm0sZttDwcXJGq2zj2NU1HObZk3fyNpXskbYQ1pIHNG/HiVh7dkCt3V2ccDD2XbnlsFg4KNhHdjzxkBmP9yF2SO6sOP1gbw4sFnWuY8Ql+TbwaWUagd8AIQDs4AbrtpkGzAeeCXz5++UUs5XrO8OrAKigHeAm4A5QDtg9FVttQfmAy2BdcBPQMz58+eJiorCYDBw++2389FHH/Hyyy+zZ88eRo+2NREXF0dkZCQJCSbS0zWbNiXz229xNGzoSO/eblm37wKcDU1n0qRwHJ0U9w33xsFBMfGNcKKisgdFr17uNG5su8r+zChfRo3yBaBTJxeefNKXBx/0ITrawgcfRGAweNCgwXP5vZV5+u677xg7dix+fn58+umn9O/fH4vFwjvvvMO0adMYMmQIo0ePZvny5Tz11FMFtjd+/Hh8fHz44osvePrppzGZqtRNetVG4ubzhH+0k6TNF7DE5j/QonI04tQg5625jiYD1zbxo3/Lmvi55/3YohAVQXnnjclkYtKkSYXKm6REV+LiLOWSN61aTs8aN7KoJG/E1cyxqYR/tJPYJSdIOxpT4PYurf1yXd6kpgcDWwXSrq63nSsUwr7KO2so8rmNQ7lkjYtLo2JPZiJZI3ITu+wkEV/sJXlHODo557jCVzL6OmOq4ZxjuZuTib7NAujbPAB3J/l3IHJX0L+MPpl/Ttdaf62UqgdMAFC20QtbAveS/XmLYCA18/utWusPlVKdgPrYgiAEeA5ocNWx3gZOYwuH/zL36wDUP3PmDDVr1mTFihVs3rwZnTnv9L59+wAYOnQoa9euBWDcOH9OnLR1CNz/gA8dO7pw6FAaq1fbroDs2JFCRgYcPpTG4UOXOw4OHkzDw+Nyf1+DBo74+po4fjyd7t1dCQx0ICXFyrFjacz/MRbrFffwt2j+RbF7j3//3Xbr8TfffEOrVq2yli9bZnvc8ssvv8xatnLlygLba9KkCSdPnmTt2rV06tSJ4cOHF6suUX4ywpKIXXqCws6S61DLFWWqUsPpieqpT+af5ZI3vr6+06Kjo6noeePu3qPw7+hVJG/E1WIWHMMSnVrwhpmcW9QoxWqEKBN9Mv+Uc5t8z21mFfoNvZpkjbhaysEoEjecK/T2TsHyiLsovsJ2feZ2qj0AeAj4G5gBPAncDDhzOQRiM/+8dM97HHDpYfCrn5WKvWo7Lm1rsViYOXMmmzZtYtSoUQwZMoSRI0eSkGB7TGTatGkcO7aEM6FfERzsmBUCWcVfUf2lb++5x4uOnS4PkFq/niNnz2W/Nf/qPqvVqxP5b2sKvfu4ccNAD+bMiebo0XScnZtf/d6UmNYak8nEsmXLMBptb5XVWvBU8v/88w+//fYbO3fu5JVXXuHnn39mw4YNdq9PlJ7ErRcK3bkF4FhPQkBUKeWSN5cuUhSUN1Onvse69fdjsSSWad7MnZvAkSNJpKWl4eKS9+DexSF5Uz1lRKaQdjy28DsYwCBXzEXVUeHPbQ4c+JKw8N/L5dxGKW/sTbKm+kraeqFI2zvW9SilSkR1UNBtH2sy/xyjlHoCeOSKdZc+Il2xXdnoadfKrnLpykZiYiLr16/n7NnLz+526tSJzp3d6dTJFV9fE+3b2z7cf/g+hiWL49i8OSlr286dXHBwgA0bkggLM3P8eDrffBOD2ZIz59wzr3qsXJnI7t0pWWGSlqoJOZ3OqVP5315ZGEOGDAFg5MiRfPPNN4wePZqEhASGDBmC2Wxm7ty5nDlzhhUrVmS74pGX0aNHk5ycTMeOHfHy8uL8+fMlrlGULXNkSsEbXcG5mVxRF1XCmsw/K3TetG0bTPv21jLOmwxOnSr8dNl5kbwRVypq1hg9nVBGGetEVHprMv+s0FnTqVMn2ndwkHMbJGuqgowi5o1LG/9SqkRUB/l2cGmt9wBjgUBsVzFWZ66KBVZie5a8DXA78FepVQk899xzdOnShcWLFxMWFkbr1tlng3N0uvwfoXt3V+64w4sTJ9L5599EOnS4fDUjqK4jb0wMxNnFwGefRrHwtzhq1zLlOpPI4MGeBASY+G5uDPPmxdK/vzsdO7qwd28Khw8Zue66a0v8uh588EGmTp1KREQEzzzzDCtXrsRoNPLKK68wbtw41q9fz6hRo1i+fDm9e/cusL3Y2FjeeOMNnnzySTw8PJg+fXqJaxRly+iRc4a1/Bg8izcejxAVSWXJG5PJE4Ph8v/RssibM6cb0qtXwZ//BZG8EVcqatYoZ7l7S1R+lSVrAJwcy/bc5ugRF8kaUSqKfG7jInkjik9pnf+zUEqpJ4FTgDswFfAHmmitw0q/POjcubPevn17gdulpUeyYcO1XL4TuHQo5UjzZpOoWXMIRqN9HxMRAmyPKMYuOl7o7Wu9fg1GV+nkqm6UUju01p3Luw57Ks+8KWzWABw48CJh4YtLtyDA1/d6mjR+GTe3RqV+LFH9aLOVC+/+hzWxcL83OTb0IuDxtqVclahoJGvsr7B5Ex+/j23bby31ekwmL5o3n4K/3wAMBulYEPYXu+xkkcbgCnr3ulKsRlRU9sqbwnyK9cT24Q+wH3i0rAKgKJwc/Wja9FWOHp1Yqsdp3+4batQo/iC/QgBoi8aamI45Id02BopF49TIC+VgJHbZiSK1JQ+MiCqkUuRNo8bjiIreSEZGwdObF5eXVxfat/uq1NoX1Yc1OQNripnUU3FYYtIweTvh3MaPmPmHC925BWD0ltl4RZVRKbLG07MN9eqO5EzoN6V4FBOdO/2Cm1vjUjyGqA602Yo1KQNzVAppp+LBoHBpUQNzdGqROrfkxEaUVIEdXFrrB8qiEHuoG/QAzk612bf/WbROK3iHIqpde5h0bokS0RZNwr9nSNh8Hp101TgHqwCjglzGTMiPOT4dR7mDS1QBlSVvnJ0CuabbMvbsfZL4+F12b99odKdD+zl2b1dUL2kn44hbFUL6qficKxcfL3LWWONLPg6cEBVBZckagCZNJuDkFMSx428BBQ/IXlSNG4+Tzi1RItY0M/F/nSZxexikZ/83Gv9XSMEjfufWZqoZgzwWL4qpGP/kKjZ//370um4bvjWut2u77u4taNL4Zbu2KaqXjLAkwj7cTvzqMzk7ty4p4gkHgCXO/p25Qoj8OTr60aXzAtq0/hx7RqlSJlq2eB+j0dlubYrqxZpmIWbpCSJm7c29cwuKlTVyVV2I8lGv3oNcd+1m3NzsO2u7b41e1A0aYdc2RfWSeiKWsPe3kbjpfI7OrSxF7ZfVYCnC3cVCXK1Kdo2aTG60b/8V+w+8QHj4kly3cXVtjJNTAGlpF7BaM/Bwb0XtOsNwd2vG2XM/EB62mLT0cBwd/alV6w7q13sMk0mmLBXFk7j5PLFLivboYWGZ5LERIcpNQMBAOnf6lZ0778Oay53DRqMbXp4dyMiIJcMcg6ODL37+A6gZcAspqWc4deojEhL2AVCjxnXUr/843l6dyvpliCrCHJtKxKx9WKJT7d62yUc6XYUoL46OfnTruowdO4cRF5fb+F0KD49WGI3upKVdAMDLswO169yHi3NtToV8RmTkSjIyYnB2rkudOsOoG/SQjLklii32j5Mkri/Co4eFZVIY3eXJFFF8Ve4Oriu1bvUhbdp8gbt7S0ChlBOBNW/lmm5/0f2av+jY4Xu6X7Oanj3W0rbtZ/j59sLZuSaNG71Iz57rub7vUa7tuZFGDV+o1p1bISEhKKVQSrFmzRoA5syZg1KKDz74oERtf/bZZ0ycOLHkRebiUt2DBw/Odb1SKmvGmokTJ6KUYsGCBXavIyM8Kd/OrdC4C9R9rxcjFrxUYFvdP7+bZh/ekPWzYz0PHGq6ATB48GCUUoSEhJS45rJ29fv/9ttvM2PGjPItSohC8vJqT48e/xJY83aMRlfAdtdvi+bv0qf3Xjp0mEvXrkvo2WMdXbosokHw07i6BuFbowedO/1M3z4H6dvnIO3aflmtO7cka0ouat7hPDu3SpI1AG5dAgHJGiHKi1KKzp1+plmzKbi4BAO2R9rr1Lmfnj030LXLEjp1nEeP7v/Qo/s/tGo1DR/vTjg716JF87e47tqtXN/3KD26/039eo9lmxG4upG8KZnkA5H5dm6VJG9c2/hnPZ4oeSOKo8p32wf4DyDAfwBaa5SS++tL6u2336ZPnz52a++zzz7jwIEDeQaB2WzGZCqdf6bz58/H29u7VNq+Uowd79x6c8DzZFhst+0a3B3wuaOJ3dq+pDTf87zceeedNG/enGuuuQaw/Tvz8/Nj9OjRRW6rPOoXwsmpJq1aTQWmSt7YgWRN0aVfSCQjNMEubV2ZNQAe/erhWNe+F/oka4QonqA6wwiqMwytrShVpe9VKBOSN0UXt+yk3dq6Mm9M/i54DWpgt7YvkbypXqrNp6KcbJScp6cnq1atIrepjQ8dOsSAAQPw9PSkfv36TJ8+Hch5peGDDz5AKcWcOXMYMWIEBw4cAGx/P3369MnavkePHvTv3586deoA8NVXX9GkSRPc3Nzo2rUrGzZsAC5fbRk5ciQdO3bEz88vx5WXhIQE7rzzTry8vLjvvvvQ2jb2yL333svYsWML9dqDg4Nxd3fnxRdfxMvLi9tvv52VK1dSt25datWqxYoVK8jIyKB27dp06NCB1OMxJP53gZZ1m9L2xX5YtZUd5/Yz9PunaPbhDfSadR+LD67O9Vjn48MZuXACrWcMotOntzFx9UzSzLbBfV9f9RFj/ngHj+vr4v1kKx4Z+yTe3t7ccsstxMfnMdYKMGLECJRSvPzyy9SrV48GDRrw999/Z3sP77nnHlq1asXdd99NWloaY8aMoXbt2nh7ezN06FBCQ0MBOHPmDD179sTPz4+XXnoJd3d3goODAUhPT2fs2LHUqVMHb29v7rrrLiIiIrLVMG7cOIKCgqhbty7r168HYMGCBdx7771s2bKFPn36kJSUxOnTp1FKMWLECLTWTJkyhfr16+Ph4UHfvn2z/u1cukLyyCOP0LBhQ8aNG1eov1MhSovkTclI1hQ+a8xRKSTviaBVcHPqNWtg96xxrO+BxwNNGPXda5I1kjWigpHOrZKTvClk3rTvQMrhaBI2nqNlnaZ0mHyz3fPGa1ADvB5twYinR0reSN6UiHwyikK79tprad26NW+//Xa25WazmaFDh3Lw4EHGjx9Pt27deOGFF/j999/zbe+pp54iKCgIsF1xeP3117PWbd68mU6dOvHWW2/xzz//8Pjjj+Pv78+HH37ImTNnuOWWW4iKisrafsWKFTzxxBMEBgYybtw49uzZk7Vuw4YNdOrUiaZNmzJ//vysAMlLWloakZGRREZGEhcXl7U8KSmJ1NRUunfvzqJFi3j88ccZ+8JYLl68yMsvv4y+mMZ919zK7t27WTNlIbu+WcOhs8e4reUA4lITGbHgZeJTE3i2+wMEeQUyetkUDoQfy3H8Z39/i9XHN/Fkt/voHdyVb3Ys4OPN32etVyaF18Bgvpr3LT/88APXX3891113HZs2bcr3dQFs2rSJl156iaioKO6//37S0i6PGfTXX3/xxBNP8OCDDzJlyhRmzJjBwIEDeemll1i2bBnDhw8HYPTo0WzatInHHnuMiIgIkpKSstp45513mDZtGkOGDGH06NEsX76cp556KlsNGzdu5Mknn+Ts2bO5Xt16/fXXcXJyws/Pj/nz5/PUU08xe/ZsXnvtNdq2bcuUKVPYtm0bQ4cOJSPj8h0GK1eu5KWXXsrztm0hROUgWZNL1oy9nDVGi+KBAXeze/duVo/9kR2frebg6SPc1sK+WYMC/yfbMWf1z5I1kjVCVEmSN/mf21jOJHFv51vYvWc3a99bxO7v1nHofCmc2zgY8OgVxKw5X0veSN6UnNa6Qn916tRJi/J16tQpDeibb75Z//DDD1oppcePH68BPXXqVL1//34N5Ph69tlns+2rtdZTp07VgJ49e7bWWutWrVpp2z/D7Mfq0KFD1rIXX3xRA3rlypVaa60nTJigAb1s2TI9e/ZsDejXXntNa631119/rQH90UcfZbXVvXt3rbXW77zzjgb0d999p7XWGtCtWrXSWmv9xhtvaED/+uuvWW0Cunfv3lprrevXr68NBoNOS0vTs2bN0oAec9NjOvSldbqWh7/2cHLToS+t0/89tUAblVE/3OkO/UrvJzSgVz0yR8+5871c36P/9X1Gb3ryZw3ofo2668NjVmhAd67TWoe+tE4ff3G1NiiDbhvYXIe+tE7X9aut3dzctNZa33rrrRrQx48f11prfe2112pAnzp1Ksff4UMPPaQBvXr1aq211vfff78G9J49e7Je75gxY7K279SpkzYYDDo1NVVrrXWPHj00oBMSErS3t7euU6eO1lrrlJQUbTAYdP369bXWWnfu3DnHa/Tw8MhWw19//aW11trJyUk3bNgwx/uvtdZubm5ZbWqt9R133KEBffToUa211vfdd58G9P79+7P2/eijj/L8N1wVAdt1BfiMripfkjXlT7Im96x58c6n9dnXN17OmgnrSz1rgrwDtZurZI1kjWRNaXxJ3pQ/yZs8zm0GjMw8twkou3ObgDpybqMlb7S2X97Iw5yiSIYNG8brr7/OF198kbXM9u8Rbrjhhmy3xQYGBmI0GgHblRCA2NjYbO3l9ShP7dq1cywrzGM/l2q5Uo0aNQCynl22WCz5tnHDDTewatUqAHx8fLKWu7i44OjoiCXU1qvvmmJrz6iMWKy2OXBreQbQv3EPFh9cTR3PAFrXbEJz/4acjQsD4M7WN3B7q8sDKQZ5BV5Vf+ZrzWU+du9bGmH4yQFSCv/a85Lbtrm953nJ6+9Ca43JZGLZsmVZf/dWa/b5ga/8+8jr7yKv9vP7N1CU+oUQFZtkjSNGi60O50iNTrNczhqLLtWsMfq7YPR0gqjcx/OSrJGsEaIqkbxxJONQLACuGbaJB4zKUCbnNjUebInhJxMk5ViV52vPi+SNuEQeURRFYjQaGT9+fLZnops3b06TJk3YsGEDu3bt4siRI3z66afs3LmTmjVr4uzszI4dO/jll1+YO3dutvYufch+9tlnbNu2LddjDho0CIA33niDL7/8km+//RYfH5+sQfsAvv32W2bNmsWMGTNQStG7d+9iv8ZatWrRv39/+vfvT6dO2Wc0s6ZbSNkTke/+D3QYSkxKHPvDj3Fn6xsB6FSnNd7Onqw5+R8nok5zJOIkn22ZR1hCZLZ93Z1c6Va3HTvPH+TTLT/w6soPsWorN1zbD/ce2T/k+vbtC8C4ceOYOnUqW7ZsKfC1vfnmm3z66acsXbqUWrVq0axZs1y3u/nmm7FarTz11FO89957bNmyhV69euHu7k7fvn05e/Ysr776Ks8880y2D/khQ4ZgNpuZO3cuZ86cYcWKFXz55ZcF1nU1Hx8fIiIimDt3LgcPHuTmm28G4IUXXuDjjz9m6dKlNGrUiKZNmxa5bSFExVfdswYg9XB0vvuXRtZc36Q7NZ/rwJXnIZI1kjVCVGXVPW/MUSmkHonJd//SyJubbrgB15a+2baVvJG8sQfp4BJFNmLEiGw9yiaTiSVLltCzZ08mT57M//73PxISEmjTpg2Ojo68++67ZGRk8Oabb9KzZ89sbT3//PMEBATwzDPP5Plhcf311zNr1iwuXrzICy+8QFBQEEuXLsXX9/KH4qBBg/jiiy8ICwvj/fffp127dqXy2lMPRaPN+V9N6BXchWCfOpgMRoa26A+Aj4snc+58l2CfOryzdhYfb/4eZwcn6l51lQNg5uDX6Ne4O59t+ZF/T25hZK9hTPr2/RzbPfHEE9x///38/fffrF27lu7duxdY/7XXXst7771HjRo1+P7773Fycsp1uwkTJvD888+zfPly3nnnHQYPHswPP/wAwIwZM+jevTuff/45NWvWxMHBIWvGlldeeYVx48axfv16Ro0axfLly4sVyOPHj8fR0ZERI0awcOFCRowYwVtvvcWePXt45ZVX6Ny5M0uWLMHBwaHIbQshKofqnDUAaWfynxHR3lnzSKc7eOPjyRgcjNm2k6yRrBGiqqvOeZNcwIV7sH/ePNZ/OBM/fzfHdpI3kjf2oIpy61956Ny5s85tZgshwDZLxsMPP8zUqVMLPWtISSRuuUDs4uN5ro9PS2T72X288Oc7dA1qy6zbJheqXffeQWScT8R8MRlrshmdYcXgZsK1UyCe19fF4Fyyp4lHjBjB3Llz2bZtG507dy5RW7t372bv3r3UqVOHP/74g+nTp/Piiy/mmOFFlC6l1A6tdcn+MkUWyRqRn7LOGoCzr24AS+6/oxU3axzre+AQ6EbaqXisielYk22P2Dg19cGzb12cGniVqGbJmqpHssb+JG9Efso6b2L/PEniunN5ri9u3nj0r0fa8VjMUalYUzLArDF6O+HWLRCPXkEoY8nus5G8qXrslTcyBpcQReBYxz3f9QfCj/HQgpdo4lufV/o8Wag2nVv64n1Tg2zLdIYV5VAxb7BMSkrijTfe4Ny5c9SsWZNnn32WN998s7zLEkKIKsWxtjvpobnfxVWcrMFkwOf2JjjUdMtapC1WQKGMBY8DU9Yka4QQovQ51rb/uY177yC8+teH/vWzlsm5jSgrcgeXEEUU9uEOzBeT7dKWU2Nv/B5uXSFPLkTFJlfV7UuyRlQ0ybsvEv3TEfs0ZlQEPNUOxyAP+7Qnqg3JGvuTvBEViTZbOT9lKzrFbJf23K6phc+tje3Slqhe7JU3FbMbVYgKzO+R1uQyEUiRKTcTfo9I55YQQoicXNsH4NiwZI8MXuJzZ1Pp3BJCCJGDMhnwG9HSLm2ZarlK55Yod9LBJUQRmbydqPFAyYPAq399lEE6t4QQQuTOf2RrDJ4lG3DWFOCCazt/O1UkhBCiqnGq74XnTcEla0SB140NCt5OiFImHVxCFINrS1/8RrZGeRTjxMOo8OhbF/futQveVgghRLWljAYCx3XBuZlPsfZ3qO1mewxeLqYIIYTIh2fvuvjc2RScjQVvfBXlaMD71sa4NKtRCpUJUTQyyLwQxeTcxIc6r16DOSqFjIspWFMyMLg5YElMR6daMPo6YwlPRls1ysmINlsxOJlwaemL0cOxvMsXQghRCRgcjPg93BpruoWMswmYY1IxuDui0y1Y4tMxuDqA0pgvpqDcHFAasGocgzxwstMjjkIIIao+t841cetck4ywJDIiU9AWjcHJgDk+HWXRGH2dyTibAEYDymQAi8bg6oBLa98Sz/guhL3Iv0QhSsjk64LJ1yX3lc19y7YYIYQQVZLB0YhTQ2+cyrsQIYQQVZpDoBsOgW65rnNpKndpiYpNHlEUQgghhBBCCCGEEJWadHAJIYQQQgghhBBCiEpNOriEEEIIIYQQQgghRKUmHVxCCCGEEEIIIYQQolKTDi4hhBBCCCGEEEIIUalJB5cQQgghhBBCCCGEqNSkg0sIIYQQQgghhBBCVGpl2sGllPJUSjXKZXnbsqxDCCFE1SVZI4QQoixI3gghRMVSZh1cSqm7gcPAb0qpA0qpLlesnlNWdQghhKi6JGuEEEKUBckbIYSoeMryDq4JQCetdXvgYeB7pdTtmetUGdYhhBCi6pKsEUIIURYkb4QQooIxleGxjFrrCwBa6/+UUn2BZUqpIECXYR1CCCGqLskaIYQQZUHyRgghKpiyvIMr4cpn1DMDoQ8wFGhVhnUIIYSouiRrhBBClAXJGyGEqGDK8g6up7jqdl2tdYJS6kbg7jKsQwghRNUlWSOEEKIsSN4IIUQFU2Z3cGmt9wCtlVJjlVI3XLE8Q2s9r6zqEEIIUXVJ1gghhCgLkjdCCFHxlOUsip8BYwBf4C2l1P/K6thCCCGqB8kaIYQQZUHyRgghKp6yfESxF9BOa21RSrkC64G3yvD4Qgghqj7JGiGEEGVB8kYIISqYshxkPl1rbQHQWicj0+cKIYSwP8kaIYQQZUHyRgghKpiyvIOruVJqb+b3CmiU+bMCtNa6bRnWIoQQomqSrBFCCFEWJG+EEKKCKcsOrhZleCwhhBDVk2SNEEKIsiB5I4QQFUyZdXBprU/ntlwp1RO4D3imrGoRQghRNUnWCCGEKAuSN0IIUfGU5R1cWZRS7bF98N8NnAIWlkcdQgghqi7JGiGEEGVB8kYIISqGMuvgUko1BYYB9wJRwM+A0lr3LasahBBCVG2SNUIIIcqC5I0QQlQ8ZXkH12Fs0+cO0VofB1BKjSnD4wshhKj6JGuEEEKUBckbIYSoYAxleKw7gDDgX6XUV0qpfsh0ukIIIexLskYIIURZkLwRQogKpsw6uLTWi7TW9wDNgTXAGKCmUupzpdTAsqpDCCFE1SVZI4QQoixI3gghRMVTlndwAaC1TtJaz9NaDwaCgN3Ay2VdhxBCiKpLskYIIURZkLwRQoiKoywHma+Rx6pfM7+EEEKIEpGsEUIIURYkb4QQouIpy0HmI4GzgDnz5yufUddAwzKsRQghRNUkWSOEEKIsSN4IIUQFU5YdXB8DfYCNwHxgg9Zal+HxhRBCVH2SNUIIIcqC5I0QQlQwZTnI/PNAe2y37D4A7FJKva+UalBWNQghhKjaJGuEEEKUBckbIYSoeMp0kHlt8y8wHvgCeBjoX5Y1CCGEqNoka4QQQpQFyRshhKhYynKQeTdgKHAP4A8sBDpqrUPLqgYhhBBVm2SNEEKIsiB5I4QQFU9ZjsF1ETiG7Rn149gGX+yilOoCoLVeWIa1CCGEqJoka4QQQpQFyRshhKhgyrKD65fMP5tlfsHl2UY0tqseQgghRElI1gghhCgLkjdCCFHBlGUH135sH/ZXfvBHYJtx5FQZ1iFEoewM38lvx34jPCmcII8g7mhyB23825R3WUKI/EnWiEolOjWahccW8t+F/3AwOnB93eu5ueHNOJucy7s0IUT+JG9EpaG1Zt3Zdfx+8ndi02Jp4t2Eu5vdTQMvmRNBVC1l2cHlnsuy+sCrSqmJWuufyrAWIfL1wbYPmHtwbtbPW8O28tux3+hUsxNPt3uarrW6lmN1Qoh8SNaISuNQ1CGeWPUEMWkxWcvWnV3HjJ0zeLjVw9zV7C48HD3KsUIhRD4kb0SlYLFaGLduHKtOr8patvXCVuYdmkffun15st2TtPBtUY4VCmE/SmtdvgUoVQNYrbXumNv6zp076+3bt5dxVaK62n1xN1O3TWVv5N58t2vv356Pr/8Yb2fvsilMiKsopXZorTuXdx2VhWSNqEji0+OZd3AeX+79Eou25Lmds9GZd657h/71ZVI2UT4ka4pO8kZUJOvOruPd/94lNCH/uQ/61+vPe73ew9HoWEaVCZGdvfLGYI9iSkJrHc3lW3uFKDef7vqUB5c/WGDnFsDuiN28uvHVMqhKCGEPkjWiojgZe5Khi4fy2Z7P8u3cAki1pDJu7ThOxp4so+qEECUleSMqAqu28uqGV3nm72cK7NwCWH1mNR/u+LAMKhOidJV7B5dS6nogpsANhSglByIP0OfnPnyx9ws0hb+jcd3ZdRyPOV6KlQkh7EWyRpQ3rTVj1oxh6JKhRKZEFno/szbz7f5vS7EyIYQ9Sd6I8vbvmX/p/mN3lp5YWqT9fjn8C/Fp8aVUlRBlo8zG4FJK7YMcvQc1gPPAg2VVhxBXOhJ9hHv/uLdIHVtXevivh/lq4Fc0r9HczpUJIYpDskZURFprbltyGyfiThRr/yUnllDTrSbPdnjWzpUJIYpL8kZURH+d+oux68YWa98MncF9f9zHVwO/opZ7LTtXJkTZKMs7uAYDQ674Ggw001p31VofLsM6RDkJCQlBKZXty9vbu8D9lFK0bt0agIkTJ6KUYsGCBTnWFUe/x/qxb8Q+4rbFFWv/2LRYnlj1BEkZSfTp0yfrdbm7u9OlSxc2bNhQ7NqudOm9Gzx4MAAjRoxAKYWM4SBEDpI11VxFzJpBTw5iyW1Lip01ALP2zuK3o79J1ghRcUjeVHMVLW+SMpJ48IUH2T9if7Hz5nTCaUb9MwqtteSNqJTK7A4urfXpsjqWqNg6dOjA+PHjAXB0LNlAhvPnzy9UkORm4dGFpFvSi7WvtmiU0Ta8QnRqNMtOLMtaN2vWLKKjo3nllVd44oknOHDgQLGOIYQoOskacUlFyRqAPRf3FGu/K7MGyDa7r2SNEOVL8kZcUlHyZvqO6RR3Arkr8+ZozFE2n9+ctU7yRlQm5T4Gl6h+/P396d+/P/3796dfv34AHDhwgH79+uHh4UH9+vV56623CvUBfe+99zJ2rO023Pj4eJ588klq166Nq6sr999/P0DW1YfIyEgiIyNRStGnj23MraudePMEB588yIHHD3D8jeMkHUkCIPFQIvtH7CfkwxBOTDrBibeyP2ay8vTKrO979+7NwIEDcXR0xGw251r30aNHGTRoEN7e3vj4+PDJJ58AsHnzZrp37467uztNmzZl/vz5Bb4HFy9epF+/fri7u+Pp6Um3bt2IiIgocD8hhKjKKkrW/H78d8w6exYUN2tOxZ3CbLW1JVkjhBAVQ0XIm969e+c65lZx82ZN6Jqs7yVvRGVSZndwCXHJypUr8ff3B2wfmKtWreKWW24hPDyct99+m1WrVvH6669Tp04dHnnkkUK3O3r0aGbPns39999Pnz59OH067wtr+yP3Uysp57Pl7q3c8entgyXJQtTKKM59e46m7zXNWp90IImA2wJwqOGQbb8d4TvwwguAZs2aZS3/3//+l+MYZrOZIUOGcPLkSSZMmEDNmjVxcnIiOjqawYMHExAQwKuvvsq///7LAw88QIsWLfK9kjNv3jz++ecfJkyYQHBwMNu3b8diyX9mLiGEqOoqQtZEJEcwYeOEHMuLmzVgu2sYJGuEEKKiqAh5sydiD3XNdXMsL27e/H7yd5xwAiRvROUiHVyizHXr1o3JkycD4OPjw5EjRzh58iT33Xcfzz33HDfddBPLli1j+fLlRQqB33//nYCAAObOnYvBkP/NiSnmlBzLLKkWUk6nEPFHBFgvL7emX/7Bo70H/oP9c+6rLUSlRAHw22+/YbFYGDVqFBMnTmTYsGGYTJf/qx05coSjR49y5513MmnSpKzlf/zxB9HR0URHRzNhwuUTon/++Yfbb789z9fSpEkTANauXYvFYmHYsGEEBgbm+/qFEKKqqwhZczrhNA1okG1ZSbIGICwpDJCsEUKIiqIi5E2GNSPHspLkTWJGIhazrVNJ8kZUJtLBJcqcn58f/fv3z/p5//79gG1QxdJgNBoB29WFxMTEPLeL2xRH4t5EPLt64nOtD+ELw0kNSUVnXL6d2OSd93+ZhPQEAHr16oWfnx8LFizgl19+4ejRo7Rs2bLAOi/dtvzggw/ywAMPZC0PDg7Od7/BgwezZcsWVq1axYoVK3jvvfdYtWpVtvdYCCGqm4qQNbk9jlLSrEmzpAGSNUIIUVGUZ97ExMXkuZ2c24jqSDq4RLlr1qwZjRo1YsmSJXz88cesXr0agEGDBhWpnSFDhjB79mweeuihrNt433zzzawP0VmzZnHkyJE899eZMz3rNE3auTTSzqYV6fgmg+2/06JFi7BYLKxatQpHR0dq166dbbtmzZrRtGlTFi9ezMSJE6lZsyaOjo7cdttt1KhRgxUrVtClSxfMZjPLli3jf//7H/Xr18/zuAsWLGDPnj00btyYVq1asXHjRs6fP1+k2oUQoqqrKllz6YRJskYIISqmssyb3Qd257l/SfPGoGx3jUneiMpEOrhEuXNwcGDJkiU8++yzTJgwAR8fH958801GjBhRpHZmzJiBg4MDS5cu5bfffuO2224DYOzYsWzcuJGZM2fy0EMP5bm/d3dv4nfE2wZfNIJrM1eSDiQV+vg1nGtwkpM8/vjjODg40KhRIyZNmpTjGXOTycTSpUsZPXo0M2bMQCnFm2++SY0aNVi2bBljx47l5ZdfxsXFhe7duxMcHJzvoJSurq4sWLCAkJAQnJ2dueeee7jzzjsLXbcQQlQHVSVrfJx8SCJJskYIISqossyb+x64L8/9S5I3BmXA08kTQPJGVCqquFOJlpXOnTvr7du3l3cZogq5Y8kdHI09atc2+wT14eN+H9u1TSHyo5TaobXuXN51VBWSNcLeDkcd5q5ld9m1TaMysvCWhTT0bmjXdoXIi2SN/UneCHu79qdriUuLs2ubj7R+hDGdxti1TSHyY6+8yX+0OiGqoLGdx9q9zQdaPVDwRkIIIaqN5r7N8XPxs2ub7f3bS+eWEEKIbB5v87jd27y3+b12b1OIsiAdXKLa6V6nO91rdbdrm2tC19i1PSGEEJXfmz3eRGG/QYb3RO7J97EOIYQQ1c+w5sNo6GXfix9ybiMqK+ngEtXSzOtncmfTO3EyOtmlvXRLul3aEUIIUXVcF3Qd0/tMp75n3oPpFoXZasaiLXZpSwghRNXgaHRk9o2zGVB/AEZltEubcm4jKisZZF5US84mZ97o/gajO47meOxxYlNjmbBhAsnm5GK11yWwi50rFEIIURX0q9+P6+tdz6HoQ6SaU/lm/zesO7uuWG11DOiYNWOvEEIIcUkN5xp82OdDLiZf5Ez8GUITQpm0eVKxL4rIuY2orOQOLlGteTl50almJ/rV78eXA76kmU+zrHUGZaBLzS74Ovvm20awZzD96vUr7VKFEEJUUkopWvq2pGPNjkzrPY07mtyBo8Exa30t11qFOpl4uPXDpVmmEEKISi7ANYDOgZ25rcltTOszjSD3oKx1DgYHrq19LW4mt3zbuKbWNbTwbVHapQpRKuQyoBCZ2ge0Z8EtCzgcfZjolGiCvYKp7V4bgJUhK/l8z+ecjDuJVVuz9mlRowXT+06XK+ollBAdybp5czi1axtWiwWfWnXoff8j1GvdrrxLE0IIu3I2OTOxx0Se7/g8h6MP42Jyoa1/WwzKwIWkC8zYMYN/Q/8lxZyStY+ryZUXOr1An7p9yq/wKkBrzd6/V7B96W8kxsbg6OJKy+v6ct19D2Ew2OexHiGEqCj61evH9XWvZ2/kXpIykmjm0wxfF18sVgu/HfuNb/d/y/nE82guj+3Yo3YP3u/1fjlWXTVEnT3D2u+/5dyRg6A1AQ0bcf2IJ/Cv36C8S6vyVEUfrLQyTKUbeTaRXStPE3owGmVQNGjnR8cb6uPp51LepQk7S0xP5N/Qf4lPj6dFjRZ0rNmxvEuq9E7u2s6SqZOxWsw51rW5/gb6PDiS6PPncHJ1xadWnXKosGKSqdvtqzJkjTndwr61Zzm44QIpiel4B7jSYWA9GnUIKO/SRCnYEb6Dw9GH8XT05Pp61+PmkP8Vd5G/tJRkfn3rVcJPHMuxzsnVjUc/+ZqkmFjM6WnUqBOEg5NzOVRZ8UjW2F9lyJvzx2LZtfI0YSfjMTkaaNq1Ju0H1MPF3bHgnUWlEpUSxdqza0k1p9KpZiea1WhW8E4iX3tW/8nqrz4Hcvaz9Lr/EVr36U9ceBiu3t54+snvcJfYK2+kg6uEDm06zz/fH87x79doUtz6YkcCG3iVT2FCVHARp0/x97dfcO7wgULv4+HrR/9Hn6FhRxkXQE467KuiZ016qpn5k7aSGJOWY13TrjUZ8EircqhKiIrPYjaz4afv2L1iGeaMwg2abDQ50Pza3gx84tlqf2eXZI39VfS82bLkBDuWn86x3MnVxD2vdcWjhnT+CpGbM/v3sOa7r4k4farQ+9SoE8RNo14ksGGTUqyscrBX3sgYXCUQG57MP9/l7NwCsJg1y2buKfuihKjgtNYc3bqReRPGFKlzCyAhKpJF701i7+oVpVSdEBXTog925tq5BXD0v3CO/hdWxhUJUfElx8cx/39j2f77wkJ3bgFYzBkcWLOauS8+Q0W/ECyEPZ0+GJVr5xZAWrKZpR/tKuOKhKj4LGYz2/9YzILJrxWpcwsg+txZfpzwAqEH9pZSddWPdHAVk8ViZdG0Hfluk5Zi5oicdAiRZf+a1Xw9aiS/f/gOFnPORxILa/U3n5ORlmrHyoSouPb+e5bIs4n5brP195NlVI0QFV/cxTAWT53M548NJ/zk8WK3E33+LFsW/WLHyoSouNJSzCz/PP+T7NjwFGLDk8qoIiEqNq01/y1ZwBdPPsDa774u9gURrTVLpr1t5+qqL+ngKqaNvx4nOT6jwO1C9kSUQTVClI6QkBCUUiilmDx5ctbyRx55JGt5Ye36axl/fT6D+MiLABy6cJG/9h8lOim5yHVpq4U9q5fnWN6nTx+UUkRGRrJmzRqUUowaNarI7QtRUcSEJbHhl6MFbhcfKR2+ovKyZ9YkxkTz0xsvcWL7FqBkWQOw4/eFOZZJ1oiq6M/P92LJKPgE/cQuObcRlZc982bNd1+z/sc5pCYkACXLm7SkRM7sy/n0l+RN0UkHVzEkxaaxf93ZQm1rNMlbLKqG2bNno7UmKSmJX3/9tUj7piYmsm7e7GzLDl24yKqDx4hOSsljr/zt/3d1sfYTojLZ+NtxCnVBUIO2yqNUovIrSdYArJ8/l8ToqKyfS5o1aclJpCXLHSuiartwMpbzR2MLta3BWPgOACEqspLkTdTZM+z8c0m2ZSXNm10rlxVrP5Gd9L4UUXqqmcXTd6Kthdv+zMFo0pILvtNLiIqsYcOGnDx5kjVr1vDzzz+TkZFBnTqXZzTcv38/LVu2xNXVFW9vbwYNGsS5c+cAmDVjOnVrB/LivMW8uXQ1S3cfZNupUDYdt43x8MWaLYz95Y8cxzRbrPy59zCTl/3Dy78t59N/NgOQkp7Bz//t4dlPvsbPz4/HH3+c5OSCr5RMnDiRmjVr4uzsTOPGjfnxxx/t8dYIUWr2rz3L6X1RBW+Y6eRuuaouKreSZM2vv/xMLT9fhjw71q5Z88aSVQTWqi1ZI6qsxJg0/vys8OP/HN0ajsVSyBMhISqokuTNzMlv0rxFS15asNyueXP/a2/JuY0dmMq7gMpm96ozxIYXvlc2JSGDRR/u4s6XOmFyqN4z8YjKq0WLFvj7+/Ptt99y6tQpbr31Vvbv35/1Qe/o6MhDDz2Er68vISEhvPPOO0ycOJEPprzFpEmTyDBbuL1jK1IzzKRkZNDQ35emNf04Gh5J/5aNqenpkeOY/xw+wT+HT9A80J82QY0Ji7Pd/rtk90F2nj5Hr6YN6HDDzXz0yad4enrywQcf5Fl/TEwMkyZNonfv3owYMYKQkBCsVvnlTFRcSXFprP/lWJH2WfHVfm4f15FaDb1LpyghSllxs+arr75i/JjRpCQnl0rW1GrSjG+++UayRlRJmxcdJzWx8OOiRp5NZMWX+xj0ZFuUQe7mEpVTcfPmjRdH8+4HH5Bhsf+5Te/mjeh6yx1MnTpV8qYEpIOriA5tulDkfaLOJrLkw10MHdMBk6N0conK6ZFHHuG5554jLS2NFStW8OKLL2atS0tL48cff2Tv3stXAPft28fulX/i6+5CREICxy9GUcfbk2sa1sPb1QU/DzeOhkfSOMCPxgG+OY538Hw4Cri/e0ecHS5/VB26cBGr1qw5cpI1Rz4GYOXKlfnW7u7uTmBgIMeOHWPTpk107dqV22+/vYTviBCl5+jWcKyWIj5yqG2zLQ55tj11W9QoncKEKGXFyZr4iIt4GuBcenqpZA1HbJM4SNaIqiY91czxnReLvF/I3ihWfnuAgY+0kk4uUWkVJ292LFuMn4cbhy9ctHve/HvoOP8eeg+QvCkJeUSxiJLjCj/N9JXCTsXLLFeiUhs2bBhGo5GgoCAGDBiQbd2UKVPYu3cvkyZN4q+//sLBwYHU1FTOHTnI8G7tubtLWzycnVhz5CQf/70JgEL9OpTHRh7OTrxw6yBWrVrFqlWr+PTTT/NtxsHBgT179vDaa68B8OSTT/L4448XpgIhykVSfFqx9tNWWPnNASxmuYonKqfiZM35Y4e5r5Sy5vHe3Vj0y8+SNaJKSk3KwGou3viNx7df5ODG83auSIiyU6y8OXqo1M5tXhtxn5zb2IF0cBWRp79LsffdvSqUv+celBMPUSl5enry7bff8uWXX2IwZP/ouDQtbmJiIosWLSIjwzbuXGJ0FH/uO0Jiajq1vT1xd3YiMS0di9WKi6MDAHvPXuDg+fAcx2tZuyZaww+bd/LfyVCW7DoAQItaASSkpnEmNYPTp0+zcOFCfv7553xrT0hIYPz48RgMBjp37oyzszPnz8svZaLi8g5wLfa+qYkZfDdhExdPx9uxIiHKRnGyJikmutSy5nBYJJGxcZI1okpy9XTEwan4T5es+fEIWxafsGNFQpSd4uRNempKqeXNqcRUObexg0rdwWXPaT4LcvF0PH/POUh8ZNFmRfj+3/cY9WU/TkccAeDw5jA2Ljhut7qEKEv33HMPgwYNyrH8tddeo3nz5syePRtfX1+8vLxITUokITICs9XKP4dP8Ov2vaRlmLmtQyuMBgMd69UhwMONzcdPs2T3wRxtXt+8Edc3b0RYfAILd+7nbIztZH1o+5Z0a1iPdTv38Pzzz7NlyxZ69uyZb90mk4lTp07x8ssv89xzz9GkSZNsnxlCFKSs8iYtOYPdq8+we/WZIu97Zd4kx6fz+8w9pCQU765jIcpTUbJGa81/SxaUTtY0qMv+sEhGjxkjWSPKRFme24QeimblNwcwp1uKtF+2cxsNO1acZs/foXarS4iyVJS8SYqNwZyWVip5071pA1av3yjnNnagdKHmHy8/nTt31tu3b891XUhICA0aNABsMyEcP36c5ORkAgMDSUxMBC73vpbE9r9OsnVxCBSjqe//fY+tR1cy7vbPqO/fDACjg4ER7/TE2d2hxLUJUVHNmzCGsBNFGyS7UJRi0KixtLi2t/3brkSUUju01p3Lu46qIr+sgbLJm/jIFBZ88B8psUU72bgkt7y55taGdLoxuER1CVGRHVz/L8s/mVYqbQc2bsqwSe9jNFXfIWsla+yvIpzb/DPvIIfWhxVr39yyxs3biQendMdgrNT3TgiRJ4vZzKynR5AcF2v3tg1GE/dMfJfaTZvbve3KxF55UyU+hfKb5jO/KT7j4+N58sknqV27Nq6urtx///0AjBgxAqUUTz31FLUCa/Pum9OISbjIrL/+x7jZQ5nw/d0s2PgpGRbblfHX593HC9/czNKtXzN+zq1MW/ws0QnZb0vcc2oD/5t3L6/9MIwjZ3YTdiqO77//nvr16+Pk5ERgYCBPPfUUFovtxGbv3r20adOGWrVq8b///Q+lFH369AEgLi6ORx55hICAgBxTicp0oaKiuBhyyq7tKYOBuq3a8vCHn1f7zi1Rfkozbxo0qs+f634hJtF+ebNy+T8AkjeiyroYYv/xTd18fOj3yFMMn/Jhte7cEuWnNLMmMKAWn336qV2zZs+hbcRFpEjWiCorKTbG7p1bBqOJJl178Pjnc6p955Y9VYnUzm+az/ymlB49ejSzZ8/m/vvvp0+fPpw+fTpbu+vXr+eBoc+gL3oz5++3ORl+gMFdHuZi7FnW7F+Is6Mrg7s8DEC6OZXE1HiubTmElbt+5LdNn/HYDZOy2jp2fjc9W9zMsm2z+XP7d7xgfBA/Pz/Gjh2Lg4MD69ev54svvuDaa69l+PDhjBgxgkOHDjFlyhQ2btyYra7Ro0fzww8/MGbMGAwGQ9ZUoq+++qpMFyoqDGd3d7sEgYunF2373cA1tw/D5OhY8sKEKIHSyps1/67lxvYPULtGQ7vmzU8rvuQVHpG8EVWWi3vOqdiLQylFgw6d6X7nfQQ2amKXNoUortI8t7mr32M4p9a0+7nNJNMTkjWiynJydUUZDGg7/Pvz9K9JhxtuptPNt6IMVeJ+owqlSnRwQd7TfOY1xSfA77//TkBAAHPnzs0xsBzA22+/jeVYEKfMFzgRto+GNVtxQ4f7yLCk89+xVRwM3ZYVAkoZuPvaZzEZHfjv6CqOXdiTra1BnR6iRd3OrNj5A9GJ4dRu4s3GvXG88847XLhwIVtt8fHx7Nq1i549e/LSSy9x7Ngxfv/996xtli1bhtlsZurUqVnLVq5cyTvvvCPThYoKo8W1fdjxx+IStdFuwE1c/8iTGAzFHwBVCHsrjbwZO2oCqftrk5aRYte8iUm0XXGPi5O8EVVT85692PjzD2hd/JMOk5Mzj386GxcP+3SWCWEPpXVuE7XRh5jIePtmTXI4nn4ukjWiynJydaNhx66c2L6lRO1cN3wEXYbcYdfx9ER2VabLMK9pPvOa4rMwateuTVxkyuVn3fP5h5h9Tc5n412dbb80GZQRZdAYHQyMHj2axMREvvvuOz766CMAUlNTs46X3z/8wMDArGlEL00lKtOFioqk69A78a5Zq0RttL9hsHRuiQqnNPLGzzcQwO55Y8gc6lHyRlRVXgGBdL31zhK10fK6PtK5JSqc0jq3SU+22D9rMm+ZkKwRVdl19z6EcwnuGjY5OdG2343SuVXKqkwHV17TfOY1xSfAkCFDuHjxIg899BDffPMNr7/+erY2tdakJWXg7OhK41ptCQk/yMpd8/ll/UdobaVV3a5Z21q1lV82fMzS/74hNimSJrXb51mro+vlG+fS09NJSEhg8eLFWcu8vLzo0KEDmzdvZurUqVlXbC4ZPHgwYWFhLF26NNtUojJdqKhIXL28Gfbm+3S4aQgOzs5A/r/YXK3X8Ifxq1u/tMoTothKI2+iz9kGD7Zn3lz9/03yRlRV1w57kJtGvYhfveAi7+tfL5jr7h1h95qEKKnSyJqMNDNms9Xu5zZOLnJuI6o+36C63Df5A1r17pc1PqNShexOUQZufGo0zm7upVihgCr0iCLYpvm82muvvcbevXuZPXs2TzzxBF5eXlnrZsyYgYODA0uXLuW3337jtttuy7ZvWooZc7rtlveHrn+FXzd+zKrd83EwOdGn9W0M7Hhf1raOJmc8XLxZf3ApwQEtuKP7U3nWaTDaTjqmT5/O888/z9SpU3nggQf4999/s7aZM2cOw4cP54MPPmDkyJH8/vvveHt7Z9VtMpn45Zdf+Pbbb2natCnjxo3Lmi50yZIlpKSk0KJFC5kuVJQrN28frh/xBNePeAKr1YLBYCQuIpzln3zIucMHct3Hr259+j/6DHWatyzjaoUoPHvnTUJ0KkY8AfvlzZX9W5I3oqpreV1fWl7XNytrLGYz237/ja0Lf8acnp5je6PJxLXDR9Du+huzLsIIUdHYO2viIlKzbsay57mNMsi5jagefGrV4canx3Dj02Oy8uZiyAn++Hga0WfP5LpP3VZtuX7E48W6CCOKTtljqtnSVNDU7aUpPdXM12PWUdBb9Pq8+0hMjePDkX8Uqt3bXuxA7SY++W6zZs0awsPD8fX1Zc6cOcybN4+PP/6YUaNGFbZ8ISq09JRktiz8maNbNqKtVmoE1aPPAyPxDapb3qVVCjJ1u32VZ9YA/PP9IQ5tvFDgdkXJm0Yd/LnxiTYFbid5I6q6Ezu2snXRryREReDs5kG7gYNoN+AmeUykECRr7K888ybybCI/T/6vwO2Kem4z4t2euHk75buNZI2o6pJiY9jw8/ec2bsLlIFaTZrR58FHcfepUd6lVQr2ypsqdQeXvTk6m6jXypfT+6Ps2q6Hr0uB20RFRTFmzBiioqKoU6cOb7zxBk8//bRd6xCiPDm6uNJr+MP0Gv5weZciRLlr3DGgUB1cReHpV3DWgOSNqPoadepGo07dyrsMIcqdX5A7PoGuxIQl261NBycDzm4OBW4nWSOqOjdvH2544rnyLqPakzu4CnDxdDyLpu3MelSxpOq18mXIs+3s0pYoH+lWK/9EJRCZYaaZmzNNXZ0ITzdzMjkVo1L08HbHzZR9YPSzqelEpJup5+yIr6P0K4uSk6vq9lXeWaO1ZtnHezhzMNpubQ6fdA3eNV3t1p4oe4cSU9gZn4yb0UBvHw9SrBaOJ6eRaLHS2NWZpm7ZH61Ltlg5lpyKi8GQY50QxSFZY3/lnTen9kSw/It9BT6hUlht+gbR656m9mlMlItEs4XVUfEkWax09HQlwNGBsLR0QlMzcDEa6O7thuNVs3KeTE4j3myhsasT7iaZEEqUnNzBVUYC6nty24sd2bzoBGePxIAGB2cjGamWIrfl7O5An+HNSqFKUVaWXYzlxSOhxJnz/vt3UooH6vgyuUkQR5JSefXoWTbE2gaQdlSKwQHeTGlSBx8H+e8nhLBRSnHTU234b+kpDm48T1qyGQCjSWExF/0spNewptK5VYlFpZt54sApNsQm5btdM1dn5rZpQJCzI1NDwphzLjIrn5q5OfNaw1oM8PPKtw0hRPXSoJ0/N49qx39LT3LxdAIAji5G0lOKfm7jE+jKNbc0tHeJogx9fTaCKSfOk2LN+3cNd6OBF4Jr8nS9mmyOTWTi8XPsSUgBwM1oYFhgDf7XqDbOxiozf52oxOQOriJISUgnI82Cu48T+9aeY8Mvxwq9b5OuNel9b7Nss4yIymV7XBJDdx6jsPFf09FEutVKjDnn3X+t3V34vWNj1kQn8GtYDNEZZpq6OfNwHT9auBfusSJRvclVdfuqSFljybCSFJeGo4sJNMx/cyvJ8TkHyc6Nk5sDtzzXjoD6nqVcpShNN20/wq7Mk4eCKKC7txubcukMMyr4oU1DGrg6MedcJLvjk3EzGrmtpje3BHjnuCIvxNUka+yvIuVNUmwaFosVjxrObPj1GHv/OVvofTveUI8ugxtgcpC7dyqrReExPHXwdKG3b+rqxOmUdNJy6T8Y6OvJV62DWRwey5KLMSRbrHTycuOh2r7Uc8l/fDYhwH55Ix1cJRB1LpFtf5wi9FAMoEFD+lV3djk4G7nl+fYENpArqJXdiL0nWREVb7f2Grs4cTwlLdsyBXzQrC7Da/va7TiiapKTDvuqyFljzrCwe3UohzaeJyUhHQdnE8lxOTu8WveuQ+975S7hym5DTAJ37j5ht/bqOztyLi2dq28E7Oblxo/tGuJmlJNTkTfJGvuryHlz/lgs2/44RfipeIwOBszplhzDtLh5O3Lbix3x8pe7hCu73lsPcSQ5reANCynIyYGzaRnZljkrmN2mIX195cKbyJ88olgB+NZx58bHL89QZbVqTu+P4viOcMxpVmo29KRlj9o4uxc88KKo+NbGJNi1vas7t8A2c/O4I6Fc5+MuVzuEEACYHIx0vimYzjcFZy2Li0jh4IbzxIQl4eLpSIvutQhsKBdSqoKNMYl2be90au53/22NS+KDU2G80biOXY8nhKi8ajfxZujoDlk/WzKsHN95kZC9kWirpm7LGjTtGoiDk3SMV3ZR6Wa7dm4BOTq3AFI1PHEghN09W+MqjzCKMiAdXHZkMCgatPWjQVu/8i5FlAKDnWYTj7h3ENbwC2A0odzdcWjcHLfhI3Fsb+uwtgKv/bKIk199ws6dO3F2dqZbt27Mnj2bwMBA+xQhhKjUvPxd6H5bo/IuQ5QCk7JT2FBw3sw5F8k1Jw/y1puTJG+EEDkYHQw06xZIs27yeVDVmOwXNQVmTbzFypu/LmLDJ9Mla0Spk25UIQrpWh8P+zXm4IjnS5Nwufl20g/sJmbsk6Tv/A+AtB1b+P6R4ezcvYdXXnmFyZMno7UmPDzcfscXQghRId3ob+c78fLJm9htm7nxphvZs0fyRgghqhMvBxNNXO34tEgB5zbvDb+bXZI1ogzIHVxCFNLrjWqzMjKewoxaZwk7T+R9N+PQtiMGDy/Sd2/DqXtvPMe+DoAyGnHpPwgAU4PGxE+ZQNK8b3Ds2JWk72aB1YrTmFe5c8zztHB34emnn8ZqzTlYvRBCiKqllbsLrd1c2J9UuEHm7ZE3vSe+w/9eHAUgeSOEENXEW43rMGzvyUJta4+scRnzGk+/9CK+jibJGlFq5A4uIQqpkaszL9SvWaR9MvbvwaFVOxw7dCV19R+kLFuQYxunrj1t2544avvz6CEAHLr2ZObpy1c2DDLblRBCVAt/dG5CUUe4KUne7GjajpgMc9Z2kjdCCFH19fH15PoaRXtCpSRZY+jSg+/OR2ZtJ1kjSoP8qxKiCFyKODiiQ6u2uA17CPcnRgOQvntHzo0uzWR66Vn4K56JX2fnge2FEEJUfFZtG4+xKEqSNxZgb3zh7hgTQghRddRxdizS9iU9t1keEVf0IoUoAungEqIICvN4YlH3TNu2CQCHhk1tfzZpAUD69s3EZFh4+Wgoi8NjSDNbin10IYQQlYem7PPmg5ALTA8J42xqujw2IoQQ1YRFFzdtipc1R5JSee3oWVZHxWO2yLmNsD/p4BKiCHoX9TbeA3tJ+mkuiV9+BJA1m4i2WEj5ezmJ33xKwrS3wGDEbfhIANweegIMBuKnvUX891/x2edfcM/QW+j04xJOJKfa9wUJIYSocFyNBrp4uhVpn5Lmzb+ffsQbH31M4z79mPDXGru+HiGEEBVTryJOolXSrIn6bhYzP/+cm2++met+XkZEeoZ9X5Co9qSDS4giaOvhSt8idHI5tGlPxoE9pO/6D+f+g3AZfIdtRUY68e++TvKyBTi0aofPtC9w7NgVAKeO3fB+91NMwY1ImvcNiV/NBLOZcDdPHth7Cmuxr7QIIYSoLJ4PrklRZnG3V95os5lvkq2sipTHSIQQoqq72d+7SLMp2vPc5riTG88ePFMaL0tUYzKLohBFNKtVMGMOn+HPiLisMVI8jQZurenDQD8vunm5seagI7cArm7u+L89gwTL5cc9/Of/WeAxnDpfg1Pna3IsP5mSxuqoeAb62XkaeSGEEBVKf19PPmpRj0nHzxN1xQDwnT1debC2H208XKjj7MiHWzKYBDi5uuE9+UMyrrgGUpK8+fpsJAMka4QQokpzMCh+ad+IZw6eYVNsYtZyfwcj99Ty5TofD67xduO33UaGAy5ubvi9PYMkO53brIlJ4GhSKk3dnO3yeoSQDi4hisjDZOTr1g04nZLGzvhknA2K3jU8cb1iAPo2nq4AXOvjzrJebTmXmk6c2cK++CSeP3K2RMffl5AiHVxCCFEN3B1Yg6EB3qyNTiDRYqWNuwtNrjoJGBHkzyRsj9D/cl1bQlLSSLdqHt1/irNpxX/0Y19icgmrF0IIURnUcnJkYYfGHEpM4VBSKt4mI9f5eOBguHwfcY/MJ1iu8/FgWS9b1iSaLayMjOP9kPC8mi6UfQnJ0sEl7EY6uIQopvouTtR3yf2W3uDgYPQVjxLWcXakDtDS3YXdCSnMPh9V7ON6moo6ebwQQojKyslgyPeixtV509LdBYBlnZrQcdPBIs/GeImHUbJGCCGqkxbuLrTIzJCrXZ01wZnnQK09XNkSl8S6mMRc9ysMDzm3EXYkY3AJUcYmNw0i0KF4H+QG4JYAb7vWI4QQouoJdHLknaZBxd7/9po+dqxGCCFEVTW7dQOcizJo5BV8TMYiT+IlRH6kg0uIMmZUigfr+BVr3xeCA6np5GDnioQQQlRFQwK8i/WLXqCjicfq+tu9HiGEEFWPm8nIIP/iDZ8yqUkdnAzSJSHsR/41CVEO7qnlW6T/fAoY3yCQsQ0CS6skIYQQVUwNB1ORTzqaujqxqkszajjIKBZCCCEK58E6RbsoYlLwcYt63B1Yo5QqEtWVdHAJUQ7qODsyuUmdQm17d6APO7q35IVg6dwSQghRNJMa1yHAseDOqjpODnzfpgHrurXA31HuFBZCCFF413i780RQwZ1cBuDpIH/292zNXdK5JUqBXJ4Topw8EuSPBl49di7Pbf7s1ISOnm5lV5QQQogqpY6zI/92aU7fbYe5mG7OdZsb/DyZ26ZhGVcmhBCiKpnUpA5mrfnmXGSu603Ajh6tZLgVUarkDi4hytHIIH9ea1grx3IFTG5SRzq3hBBClJivo4nFHZpQJ5eTivYersxsXq8cqhJCCFHVTGkaxIO1fXMsdzIovmodLJ1botTJHVxClLNR9Wsy0M+LHy9EcS41g3oujtxXqwaNXJ3LuzQhhBBVRENXJ9Z3a8Gi8BjWxyRgUoqb/L24wdcLk6GY018JIYQQV3m/WV2GBdbgl/AYojPMNHN1ZnhtXwKlc0uUAengEqICaOrmzMTGhRuTSwghhCgOV6OB4bV9GZ7L1XUhhBDCXjp6udHRS55EEWVPHlEUQgghhBBCCCGEEJWadHAJIYQQQgghhBBCiEpNOriEEEIIIYQQQgghRKUmHVxCCCGEEEIIIYQQolKTQeaFEJVaREQEmzdv5tSpUxgMBpo1a0a3bt3w8vIq79KEEEJUERkZGezcuZM9e/aQnJyMv78/Xbp0oWnTpuVdmhBCiCokNDSUrVu3cvbsWRwdHWndujVdunTBxcWlvEurFKSDSwhRaR09epSffvoJq9WatWzTpk3s2rWLLl26kJSURI0aNejatSsODjI1sRBCiKLLyMhg1qxZREREZC2LjY3l2LFjtG3bFjc3N7TWtGvXjlq1apVjpUIIISqzrVu3snz58mzL/vnnH3bu3Em7du1ITEykVq1adOrUCaVUOVVZsUkHlxCiUoqMjOTHH3/MdV1KSgrr1q3L+nn16tU0adKEQYMG4e3tXUYVCiGEqAqu7ty60t69e7O+37JlC+7u7tx00020aNECg0FGAhFCCFE4R44cydG5dUlsbCxr167N+vnPP/+kXbt29O/fHzc3t7IqsVKQDi4hRKX09ddfF3pbrTVHjx7l6NGjtG3bliFDhsgdXUIIIQq0devWPDu3cpOYmMivv/6Kt7c3t99+O/Xq1SvF6oQQQlQFZrOZn3/+udDbW61Wdu3axZ49e7jmmmvo37+/XFTJJO+CEKJSsVgszJ8/n9TU1GLtv3fvXhYuXJjtsUYhhBDiaidOnGDFihXF2jc2NpYffviB8PBwO1clhBCiKklLS2PWrFnFOjexWq1s2rSJVatWlUJllZN0cAkhKpV///2XI0eOlKiNQ4cOMX36dDZv3ozW2k6VCSGEqCqio6P56aefSpQR6enpfPHFFyxYsICYmBg7VieEEKKqWLJkCRcvXixRG5s3b+aTTz7J9th8dSUdXEKISiM9PZ1t27bZpa2EhAT++usv/vjjD7u0J4QQourYtm0bGRkZJW5Ha83+/fv56quviI+Pt0NlQgghqoro6GgOHTpkl7YiIyNZuHAhmzZtskt7lZV0cAkhSk1ISAhKqayvGjVqMGzYMKKiogAIDg7G3d290O2Fh4eTlpZm1xq3b9/OyZMni73/nDlzUErxwQcfAEV/TUIIISqe06dP27W95ORkFixYYNc2hRBCVG6hoaF2f5pk1apVxMbG2rXNykQ6uIQQpa5Dhw78+OOP9O3bl59//pnx48cXqx2TqXTmxVi0aFGptCuEEKJs2PuCSmlMRHLmzJkSdZzJBRUhhChflSFrtNb89ttvxd6/smeNdHAJIUpd7dq1uffee5k8eTJgm5XqaqtXr6Zx48Y4Ozvj5+fHsGHDSEhIAODLL7+kbt26BAcH89FHH2XdejtnzhwmTZrE6tWref/995k9ezahoaF88sknvPfee+zatQuAixcv8umnnzJlyhTeffdd5s2bl+1RkYSEBA4cOIDFYmHFihV06tQJV1dXgoKC2Lx5MwDffvstzZo1w83NjR49erBz584CX/fGjRtp27Ytzs7O+Pv7c++995bsjRRCCJEve11QadGihZ0rs1m/fj3Jycml0rYQQoiyYa+sadSoUal0coWGhpbK3WGVgXRwCSFKXUZGBhERESxevBgg12nT3d3defrpp5k5cyb33nsvP//8MzNnzgRg/Pjx+Pj48MUXX3DHHXfkmAY3JiaGJk2acObMGX788Ue6du2K1pqVK1cCoI2a1u1bc9NNN9G1a1dOnDjBmjVrsrXx66+/8sorr3DLLbdw/vx5pk6dylNPPYXFYmHNmjWMHDmS4OBgXnvtNaKiorjlllsKnMnx/fff5+TJk3z00Ue8/vrr+Pn5FfMdFEIIURj2uqDSu3dvZsyYYfcLKsePH2fq1Kn8+OOP/Pzzz3JBRQghKiF7ZY2npyfTp0+3e9YAfPPNN8ycOZOZM2dWq6wpned9hBDiCitXriQgIACAOnXq8Pbbb+fY5v/t3W1sVNW+x/HfmmHaaRlsS8tDNVA4bQnooQ/QtBzFtjKmaNJTwWPCvdd6nYgmJjXGF0cT8SZcSLARQm4wKEhC6uUNKoq5CSfnSKEVyy0x8SApCLHXVvtgaEWllEJxOu2+L9oOfRhGoNOH6Xw/r2avtffaa/fF/NP/f+1Z3d3devfdd9XQ0OBvO3v2rCQpPT1djY2NOnHihHp7e5WZmTnsWrfbrcuXL6uurk5LlixRbm6uvvnmGzU3N6tqVpUu2i+q9X9adaPlZkIq0G4l586dU09Pj0pKSlRWVuZvf/XVV/3PMZg0k6Tz588Hfe709HQdOXJER48e1YoVK4aNCQAIvTspqLhcLp09e1a7d+/W8uXL9cYbb+i1115TSkqKysrKdOzYsVsWVOrq6vzV+6qqKh09elTZ2dmy2+3KzMxUbGysOjo6dPLkSX3++ecqKSnxj2FZlk6dOqW9e/cqMTFRO3bsUEdHx7CCSlFRkTwej95//32VlJTou+++C/rcQwsqXq9X9fX1Y/9jAgACClWs2bp1qw4fPjxqU5NQxBqpv6iyZ88excXFRUysIcEFYNzl5eVp27Ztmj17tu6//35FR0ePOuf1119XY2Oj9uzZo9mzZ2vDhg3+FVJVVVX65JNPdPr0ae3bt0/z5s3Tc88957/W6XTKbrdLkn9sn90nSboUfUmXPrqkGy03NHf9XMWkxqjlv1rk8/luOd+Wlhb9/PPP/hVXg8t7d+7cqYyMDElSX1+fFi9eHHQ73u3btys/P1+1tbXav3+/ysvL1draqvj4+Nv90wEA7kCoCipffPGFHA6H/zt/ULCCSnd3t3w+n86dO6f29nb/NYEKKg0NDfL5fFq/fj0FFQAIM6GKNdXV1fL5fHdUvL/TWNPb26uHHnpIGzdulNPplDS9Yw2vKAIYd0lJSXK73crOzg6Y3JL6k0iWZamzs1OHDh0a1vfKK6/o+vXrWrFihWJiYvzLe4PpcnTdHFv9Caq+G326+s+r6uvt87cNlZqaKrvdrpqaGm3ZskXl5eU6efKkiouLJUkHDx5Uc3OzvvzyS7388stKSEgIOoc333xT3377rR544AEtWLBA165dY5t4ABhHeXl5OnbsmE6fPq2GhgZlZWWNOmdoQeXDDz+UpGEFlZ07d+qee+7R8ePHdfDgwWHXBiqoGGMk9cexmpoatbe3q7CwUKWlpbLZbEELKj/++OOw46EFlcrKSlVWVuqzzz7T4sWLgz739u3b9emnnyo9PV379+9XTk5ORO+iBQDjKZSxpqqqatxjjc/n04ULF/zH0znWkOACMCWUl5drwYIF2rVrl7Kzs4f1dXR0aPPmzXrxxRcVFxentWvXBh3rquOqfObml/zcP89VdHK0Lp+8LLvLLluMTT7b6CCQmJioDRs2aNasWXrvvff0zjvvyG63q7CwUBUVFerq6lJZWZn27dunBx988HefyWaz6e2339bGjRtVX1+vLVu2BFzCDAAIjVAVVNasWSOn03lbBZVAvF6vLly4oL6+voD9gwWV6upqbdu2jYIKAISRUMWalStXyuVyjXusqamp0Y4dOyIi1vCKIoBxs2jRoqC7d/zwww/+z2vWrFFzc7P/eNOmTf7Phw8f9n/2er06cOCAWltb5fF4ht1r8+bNkqRfza/6w+t/8PfNmDVD6eXp/uN5f5mn5b8slwJ8H6enpys9vf/cF154Qffdd58kyePxDLvfoJHtQ59p06ZNw54DADD5ysvL5fF4tGvXLpWVlenjjz/29w0WVK5evark5GStWrXqjsZ++OGH1d7erjNnzmjlypW3/MdnsKBSXV2trVu3as6cOSosLNTq1atVUVGht956S2VlZZo7d67cbvfv3newoNLe3q7ExEQKKgAwyW431qSkpCg/P/+Oxr6bWHPo0CGdOHFi2scaM9W3jszJybG++uqryZ4GgCnE6/Xq1KlT+vrrr9XZ2TmqatFjenRk4RH12QJXMyQp9Uqqsn7NCnqf4uJi5eTkhGLKIWeM+adlWVNzcmGIWAMgkIaGBtXW1qqpqSno6x9jkZSUpJdeemlcxh4rYk3oEW8AjNTV1aXa2lrV1dXp2rVrQRcIjMUzzzyj1NTUcRl7rEIVb1jBBSDsREVFqaCgQAUFBWpra9PevXuH9Tssh6L7otVt677lGL84f/nd+9yqGgIAiAypqan+fwYqKirU1NQU8nsM/ugvACAyuVwuFRUVqaioSOfPn9dHH300LveJhHjDb3ABCGvz588ftfOIJMX4YoJe12t6g/ZHRUVpyZIlY5obAGD6cLvd/h/9DaXly5eHfEwAQHhaunSpUlJSQj5uYmKi7r333pCPO9WQ4AIQ9p544gk98sgjmjlzpr8t8UZi0GsSfwve/+ijj7KCCwDgt3DhQj377LNatGiRv21wV6u7NX/+/FEbqwAAIpfNZtPTTz+tVatWjdpB8W4ZY7R27doxjxMOeEURQNiz2WwqKCjQ6tWr1dnZqejoaDVdaVJpZam8lnfU+cYySruSFnAsh8OhJ598UsuWLRvvaQMAwszChQvl8XjU1dUln8+nmTNn6sCBA2ppabnjsTIzM/X4448rKipqHGYKAAhXUVFReuyxx+R2u9XV1aWYmBjV19cP23jrdrlcLj311FPDijPTGSu4AEwbdrtdCQkJio2N1bLkZdr96G7FOeKGnePodSjvpzzF9cSNut7hcMjj8ZDcAgAE5XK5FB8fL4fDodLSUqWlBS6a3EpOTo7Wr18fEb+HAgC4Ow6HQwkJCXI6ncrIyNC6des0Y8btr1FyuVx6/vnnIya5JbGCC8A09qd7/6TjG46r8vtKnfn+jGzXbUrrS9Nvjt/U1tYmr9ernp4eGWO0bNkyFRQUaN68eZM9bQBAGImOjlZpaakuXryouro6tbe3KzY2VpZlqa2tTV1dXfJ6vbIsS0lJScrLy5uyO/QCAKaurKwsLV26VHV1dWpsbFRfX5+cTqeuXLmin376ST09PfL5fJoxY4YyMjKUn5+v+Pj4yZ72hCLBBWBai7ZHqzitWMVpxQH7fT6fbDabbDYWtAIA7l5ycrKSk5MD9lmWpd7e3juqvAMAMJLT6VRubq5yc3MD9vt8Ptnt9oj4va1AiLIAIhr/bAAAxpsxhngDABh3kR5rWLIAAAAAAACAsEaCCwAAAAAAAGGNBBcAAAAAAADCGgkuAAAAAAAAhDVjWdZkzyEoY8wlSU2TPQ8AmGJSLMuaM9mTmC6INQAQELEmxIg3ABBQSOLNlE9wAQAAAAAAAMHwiiIAAAAAAADCGgkuAAAAAAAAhDUSXIgYxphFxphzI9r+0xjzV2PM+8aY68aYWUP6dhljLGNM0pC29QNtS0eM222MOWOMOW+M2WuMsQ30/cMY02GMOTIRzwgAmFzEGgDARCDeAKOR4AJu+k7SE5I08CX+iKQfR5zzr5JOSvqXEe0NlmVlScqQdL+kdQPtOyQ9Mz7TBQCEIWINAGAiEG8QcUhwATcdlLRh4HOhpP+V5BvsNMa4JD0kaaNGBwFJkmVZPkm1ktIGjo9LujpuMwYAhBtiDQBgIhBvEHFIcAE3/Z+kOcaYBPVXMz4Y0b9O0j8sy6qX9KsxZsXIAYwxsZLcks6O81wBAOGJWAMAmAjEG0QcElyIJNZttB9WfwUjT1LNiPOGBoYPBo4HpRpjzqi/MvI3y7L+PubZAgDCEbEGADARiDfACDMmewLABPpFUsKIttmSvh9y/IGk05L+27KsPmOMJMkYkyhpjaQ/GmMsSXZJljHmtYHrBt9TBwBENmINAGAiEG+AEVjBhYhhWVaXpIvGGLckGWNmS3pM/T+sOHhOs6Q3JL074vKnJB2wLCvFsqxFlmUtUH/wWD0hkwcAhAViDQBgIhBvgNFIcCHS/Luk/xhYclslaYtlWQ1DT7As672RbepfsvvpiLZPJP1bsJsZY2okHZLkNsa0GmPWjmXyAICwQKwBAEwE4g0whLGsW726CwAAAAAAAEx9rOACAAAAAABAWCPBBQAAAAAAgLBGggsAAAAAAABhjQQXAAAAAAAAwhoJLgAAAAAAAIQ1ElwAAAAAAAAIayS4AAAAAAAAENZIcAEAAAAAACCs/T/ZbUWYil9QaAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1483.92x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(adata, color = ['cell_type', 'predicted_labels', 'majority_voting'], legend_loc = 'on data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "correct-acrobat",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 374.4x324 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "celltypist.dotplot(predictions, use_as_reference = 'cell_type', use_as_prediction = 'majority_voting')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "inappropriate-walker",
   "metadata": {},
   "source": [
    "## Examine expression of cell type-driving genes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fatty-theater",
   "metadata": {},
   "source": [
    "Each model can be examined in terms of the driving genes for each cell type. Note these genes are only dependent on the model, say, the training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "closed-snapshot",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Any model can be inspected.\n",
    "# Here we load the previously saved model trained from 2,000 immune cells.\n",
    "model = models.Model.load(model = 'celltypist_demo_folder/model_from_immune2000.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "working-afghanistan",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['DC1', 'Endothelial cells', 'Follicular B cells', 'Kupffer cells',\n",
       "       'Macrophages', 'Mast cells', 'Neutrophil-myeloid progenitor',\n",
       "       'Plasma cells', 'gamma-delta T cells', 'pDC'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.cell_types"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "atlantic-grain",
   "metadata": {},
   "source": [
    "Extract the matrix of gene weights across cell types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "renewable-aquarium",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 16201)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = model.classifier.coef_\n",
    "weights.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "difficult-subcommittee",
   "metadata": {
    "tags": []
   },
   "source": [
    "Top three driving genes of `Mast cells`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "present-canon",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CPA3', 'TPSAB1', 'TPSB2'], dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mast_cell_weights = weights[model.cell_types == 'Mast cells']\n",
    "top_3_genes = model.features[mast_cell_weights.argpartition(-3, axis = None)[-3:]]\n",
    "top_3_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "public-flour",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1614.38x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check expression of the three genes in the training set.\n",
    "sc.pl.violin(adata_2000, top_3_genes, groupby = 'cell_type', rotation = 90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "immune-empire",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1614.38x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check expression of the three genes in the query set.\n",
    "# Here we use `majority_voting` from CellTypist as the cell type labels for this dataset.\n",
    "sc.pl.violin(adata_400, top_3_genes, groupby = 'majority_voting', rotation = 90)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
